微藻综合品质信息快速无损获取技术和方法研究
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摘要
微藻在能源、食品工业、生物技术、医药工业、动物饲料、环境检测、污染治理等领域拥有广阔的开发应用前景。微藻具有生长繁殖快、光合作用效率高、倍增时间短、单位面积产量高等优点。随着能源、粮食、环境问题的日益突出,微藻生物质产业的发展已经得到了世界各国的广泛重视。目前微藻生物质产业中遇到的关键问题之一是如何提高微藻生产效率、增加产品经济价值并降低生产成本。为此本文提出“数字微藻”的概念。数字微藻的实现,即通过高科技手段,在微藻育种、养殖、收获和产品加工销售等环节快速、准确地获取微藻相关品质信息,用于品质监控,进行系统优化,最终实现微藻数字化科学生产与管理,从而提高微藻生产效率、保证质量、降低成本,增加经济收益,其中微藻品质信息的快速准确获取是实现微藻数字化科学生产与管理的基础和关键。数字微藻必将成为世界微藻生物质产业发展的研究重点和热点。而传统微藻信息获取方法耗时费力,不适合微藻生物质产业现代化生产的发展需要。
本论文应用显微成像、光谱分析、高光谱成像、核磁共振等技术,结合多种图像处理技术和化学计量学算法,针对微藻育种、养殖、收获和产品加工销售等环节对相关品质信息的需要,研究了微藻形态、生命和品质信息的快速获取理论和方法,为微藻生物质产业高效生产和系统优化奠定了基础。本论文的主要研究内容和成果如下：
(1)提出了微藻藻丝形态特征快速提取方法。通过对藻丝显微图像进行预处理,获得垂直朝向的单一藻丝二值化图像,以便藻丝形态特征参数提取；采用离散曲线演化技术(DCE)技术获取藻丝特征顶点；提出了一个新的算法用于去除骨架枝权,并获取了藻丝长度信息。该算法能够解决传统骨架提取算法在提取藻丝骨架时容易引入枝权的不足；实现了包括螺旋度、藻丝宽度、螺径、螺距、螺环数和紧密度等藻丝形态特征参数的快速获取。螺旋程度测量的平均误差为4.7%,藻丝宽度测量的的平均误差为6-2%,螺径测量的的平均误差为5.6%；开发了螺旋藻形态特征快速提取软件。该软件与人工测量方法比检测时间从5分钟减少到30秒,检测精度从93%提高到99%。
(2)首次应用光谱和高光谱成像技术,建立了光谱和高光谱图像信息与微藻生命信息的定量关系模型,实现了微藻主要生命信息的快速获取。光谱检测中,透射光谱测量法和透反射光谱测量法的检测能力要优于反射光谱测量法和反应器外部光谱测量法；基于光谱技术的干物质重、油脂单位体积含量和油脂单位质量含量最优模型的rpre2分别达到0.9836,0.9777和0.9487；光谱特征变量模型平均变量个数为9.41个。和全波段模型相比有99.62%的变量被去除,而模型rpre2平均值仅下降4.19%;12个光谱模型中有8个模型的的UVE-SPA特征变量选择结果要优于直接进行SPA计算。说明LIVE可以有效提高SPA对光谱特征变量提取的效率；基于高光谱成像技术的干物质重、叶绿素单位体积含量、叶绿素单位质量含量、叶绿素a单位体积含量、叶绿素a单位质量含量、叶绿素b单位体积含量、叶绿素b单位质量含量最优模型的rpre2分别达到0.9891、0.9882、0.9242、0.9895、0.9444、0.9780和0.9282；高光谱图像特征变量模型平均变量个数减少到了7.43个(99.7%的变量被去除),而rpre2平均值则达到0.9550(全波段模型为0.9573)；7个高光谱光谱图像模型中有5个的特征变量最优提取算法为UVE-SPA。说明UVE可以有效提高SPA对高光谱光谱图像信息特征变量提取的效率；基于高光谱图像,获得了微藻生命信息检测指标的藻液分布图。结果表明高光谱成像技术在微藻生命信息获取能力上要明显优于RGB图像。
(3)研究构建了藻油ω-3多不饱和脂肪酸含量快速获取方法和系统。核磁共振技术获得了最优的藻油(o-3多不饱和脂肪酸(DHA和EPA)含量检测结果。模型rval2,分别为0.9625和0.9674；可见-短波近红外光谱、长波近红外光谱和和中红外光谱最优检测模型对藻油DHA含量检测的屹7分别为0.9190、0.9232和0.8748,对EPA含量检测的吃,最大值分别为o.9213、0.8757和0.8857,但均未能达到核磁共振技术的检测精度；基于可见光激光源的拉曼光谱技术不能准确测量藻油DHA和EPA含量。其模型RMSECV分别高达21.2707和1.8529,是核磁共振谱模型RMSECV值的1.67和2.68倍；特征变量选择能有效提高藻油DHA和EPA含量检测精度,模型RMSECV平均值能够降低18.70%和29.03%；10个藻油品质指标检测模型中有9个的最佳特征变量选择算法为UVE结合SPA,说明UVE可以有效提高SPA在藻油品质指标检测过程中特征变量提取的效率。
(4)研究建立了藻粉品质信息快速获取方法与模型。研究了藻粉类别的可见-近红外光谱快速检测方法。基于7个特征波长的SPA-LS-SVM模型分类正确率达到100%。研究了藻粉蛋白质含量的可见-近红外光谱快速检测方法。UVE-SPA-MLR为最佳检测模型,其中rpre2、RMSEP和RPD分别为0.9750、0.2344和6.2206,检测精度能够满足实际要求；研究了藻粉辐照剂量的光谱快速检测方法。UVE-SPA-BP-ANN模型为最优的藻粉辐照剂量检测模型,其中rpre2、RMSEP和RPD分别为0.9850、0.6414和8.1366,检测精度能够满足实际要求；研究了藻粉掺假信息的光谱快速检测方法。DVA分析结果表明短波近红外光谱比全波段光谱更适合被用于藻粉掺假信息的快速获取,而LS-SVM算法要优于PLS和PLS2算法。对于单一掺假物、两组掺假物和多组掺假物情况下面粉和绿豆粉LS-SVM短波近红外光谱检测模型的rpre2分别为0.9966、0.9430和0.9740,以及0.9903、0.9474和0.9705。
Microalgae have broad application prospects in many fields such as fuel, energy, food industry, biotechnology, pharmaceutical industry, animal feed, environmental monitoring, and pollution control. Microalgae have excellent qualities of fast growth and reproduction, high photosynthetic efficiency, short doubling time, high production per acre, low greenhouse gas emission and little or no competition with food production. Because of the growing threats of energy, food and environmental issues, microalgal biorescoure industry has gained more attention worldwide. At present, one of the key questions is how to improve the growing efficiency, increase the value of algal products and reduce the production cost. This thesis proposed a new concept of DIGITAL MICROALGAE, which means using high-tech methods to rapidly and non-invasively measure the microalgal growth information and qualities of algal products in the processes of microalgal breeding, cultivation, harvesting, processing and sales. The obtained information can be used for the optimal decision-making control and system operation, and ultimately optimizes the production and management of microalgae, and improves production efficiency, ensures the quality of products, reduces costs and increases economic benefits. The foundation and key in the process of Digital Microalgae is the rapid and non-invasive determination of microalgal growth information and qualities of algal products. Digital Microalgae will become the research priorities and hot spots of the world. At present, however, conventional off-line analyses are rather time-consuming and inefficient which can not meet the needs of modern production of microalgal biorescoure industry.
In this work, according to the requirements of relevant quality information of microalgal in the processes of breeding, cultivation, harvesting, processing and sales, microscopy imaging technique, spectral analysis technique, hyperspectral imaging technique, nuclear magnetic resonance (NMR) technique combined with image process algorithms and chemometrics were used for rapid and non-invasive determination of comprehensive quality information of microalgal such as microscopic morphological features, growth information, qualities of algal oils and algal powders to further provide information support for the high efficient production and system optimization of microalgal biorescoure industry. The main research contents and results are shown as follow:
(1) Microscopy image processing algorithms were proposed to rapid measure morphological features of Spirulina microalga filaments. After the image preprocessing, binary images of each filament at vertical position were obtained. The filament rotation was applied to make the filament characteristic parameters extraction easier. The vertices of filaments were determined based on discrete contour evolution (DCE) algorithm. Because traditional algorithm of skeleton extraction has the disadvantage of bringing in branches, a new algorithm was proposed to reduce the branches and obtain the Spirulina length based on the main skeleton. Morphological features such as length, diameter of helix, degree of spiralization, width of filament, pitch, helix number, and helix pitch were obtained based on the vertices of filaments. The errors between manually obtained and automatic calculated values were 4.7%for degree of spiralization,5.6%for diameter of helix and 6.2%for width of filament, respectively. Furthermore, a Spirulina Morphological Feature Extraction System Software was developed. The feature extraction time is about 30 seconds and the measurement accuracy was 99%by using the software, while manual measurement usually takes five minutes with the accuracy of 93%.
(2) Spectroscopy and hyperspectral imaging techniques were for the first time applied to establish the quantitative relationship models between the spectra and hyperspectral image information and algal growing information respectively and realized the rapid determination of algal growing information. In the spectral analysis, transmittance model and transflectance model were better than reflectance model and reflectance measured outside the bioreactor model. The best coefficients of determination of prediction (rpre2e)values of the models were 0.9836,0.9777 and 0.9487 for the spectral analysis of the dry weight, lipid content per unit volume and lipid content per unit weight. There were average 9.41 variables for the spectral efficient variable models. Compared to the whole spectral models,99.62%of the variables were eliminated, while the average rpre2 values of the spectral efficient variable models only decreased 4.19%. In all the twelve spectral efficient variable models, for eight models, uninformative variable elimination combined with successive projections algorithm (UVE-SPA) performed better than using SPA directly in the variable selection processes. It shows that UVE could effectively improve the variable selection accuracy of SPA. The rpre2 values of the hyperspectral image models for the measurement of dry weight, chlorophyll content per unit volume, chlorophyll content per unit weight, chlorophyll a content per unit volume, chlorophyll a content per unit weight, chlorophyll b content per unit volume and chlorophyll b content per unit weight were 0.9891,0.9882,0.9242,0.9895,0.9444, and 0.9780 respectively. There were avaerage 7.41 variables for the hyperspectral image efficient variable models. Compared to the whole spectral models,99.7% variables were eliminated, while the average rpre2 value of the spectral efficient variable models was 0.9550 (0.9573 for the whole spectral model). In all the seven hyperspectral image efficient variable models, for five models, UVE-SPA was better than using SPA directly in the variable selection processes. It shows that UVE could improve the hyperspectral image variable selection accuracy compared to using SPA directly. Moreover, the quality distribution maps of microalgal slurry were obtained based on the algal hyperspectral images. The results show that hyperspectral imaging technique is better than RGB images to measure the growing information of algae.
(3) Rapid determination method and system of the content of w-3 PUFAs in algal oil were proposed. NMR obtained the best DHA and EPA prediction models. The best coefficients of determination of validation (rpre2) values of two models were 0.9625 and 0.9674 respectively. The best rpre2, values of visible and short-wave near infrared spectral model, long-wave near infrared spectral model, and mid-infrared spectral model were 0.9790,0.9232 and 0.8748 for DHA analysis,0.9213,0.8757 and 0.8857 for EPA analysis respectively. However their results were not as good as those of NMR models. The Raman spectroscopy with 514 nm light laser didn't perform well for DHA and EPA prediction. The RMSECV of their models were 21.2707 and 1.8529, which were 1.67 and 2.68 times of those of NMR models. The effective variable selection can improve the DHA and EPA prediction accuracy. Models'average RMSECV values decreased 18.70%and 29.03%respectively. In all the ten efficient variable models, for nine models, UVE-SPA was better than using SPA directly in the variable selection processes. It shows that UVE could improve the variable selection accuracy compared to using SPA directly in the algal oil analysis.
(4) Rapid determination method and models of the quality of algal powders were established. Based on visible and near infrared spectroscopy, SPA-least square support vector machine (SPA-LS-SVM) with seven effective variables reached 100%correct answer rate for the algal powder classification. Visible and near infrared spectroscopy was used to predict the protein content in algal powders. UVE-SPA-multiple linear regression (UVE-SPA-LS-SVM) obtained the best result with the rpre2, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9750,0.2344, and 6.2206, respectively. The detection accuracy can meet the practical requirements. Spectroscopy technique was used to determine the irradiation dose of algal powders. UVE-SPA-Back-Propagation Artificial Neural Network (UVE-SPA-BP-ANN) obtained the best result with the rpre2, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9850,0.6414, and 8.1366, respectively. The detection accuracy can meet the practical requirements. Visible and near infrared spectroscopy was used to predict the adulterant contents in algal powder. The design value analysis (DVA) indicated that for quantification of adulterants in algal powder, short-wave near infrared spectroscopy outweighs full spectra, and LS-SVM models outweigh PLS and PLS2 models. The rpre2 values of LS-SVM models based on NIR spectra were 0.9966,0.9430, and 0.9740 for flour detection and 0.9903,0.9474, and 0.9705 for mung-bean detection, while single adulterant, two adulterants and multiple adulterants were considered.
本论文应用显微成像、光谱分析、高光谱成像、核磁共振等技术,结合多种图像处理技术和化学计量学算法,针对微藻育种、养殖、收获和产品加工销售等环节对相关品质信息的需要,研究了微藻形态、生命和品质信息的快速获取理论和方法,为微藻生物质产业高效生产和系统优化奠定了基础。本论文的主要研究内容和成果如下：
(1)提出了微藻藻丝形态特征快速提取方法。通过对藻丝显微图像进行预处理,获得垂直朝向的单一藻丝二值化图像,以便藻丝形态特征参数提取；采用离散曲线演化技术(DCE)技术获取藻丝特征顶点；提出了一个新的算法用于去除骨架枝权,并获取了藻丝长度信息。该算法能够解决传统骨架提取算法在提取藻丝骨架时容易引入枝权的不足；实现了包括螺旋度、藻丝宽度、螺径、螺距、螺环数和紧密度等藻丝形态特征参数的快速获取。螺旋程度测量的平均误差为4.7%,藻丝宽度测量的的平均误差为6-2%,螺径测量的的平均误差为5.6%；开发了螺旋藻形态特征快速提取软件。该软件与人工测量方法比检测时间从5分钟减少到30秒,检测精度从93%提高到99%。
(2)首次应用光谱和高光谱成像技术,建立了光谱和高光谱图像信息与微藻生命信息的定量关系模型,实现了微藻主要生命信息的快速获取。光谱检测中,透射光谱测量法和透反射光谱测量法的检测能力要优于反射光谱测量法和反应器外部光谱测量法；基于光谱技术的干物质重、油脂单位体积含量和油脂单位质量含量最优模型的rpre2分别达到0.9836,0.9777和0.9487；光谱特征变量模型平均变量个数为9.41个。和全波段模型相比有99.62%的变量被去除,而模型rpre2平均值仅下降4.19%;12个光谱模型中有8个模型的的UVE-SPA特征变量选择结果要优于直接进行SPA计算。说明LIVE可以有效提高SPA对光谱特征变量提取的效率；基于高光谱成像技术的干物质重、叶绿素单位体积含量、叶绿素单位质量含量、叶绿素a单位体积含量、叶绿素a单位质量含量、叶绿素b单位体积含量、叶绿素b单位质量含量最优模型的rpre2分别达到0.9891、0.9882、0.9242、0.9895、0.9444、0.9780和0.9282；高光谱图像特征变量模型平均变量个数减少到了7.43个(99.7%的变量被去除),而rpre2平均值则达到0.9550(全波段模型为0.9573)；7个高光谱光谱图像模型中有5个的特征变量最优提取算法为UVE-SPA。说明UVE可以有效提高SPA对高光谱光谱图像信息特征变量提取的效率；基于高光谱图像,获得了微藻生命信息检测指标的藻液分布图。结果表明高光谱成像技术在微藻生命信息获取能力上要明显优于RGB图像。
(3)研究构建了藻油ω-3多不饱和脂肪酸含量快速获取方法和系统。核磁共振技术获得了最优的藻油(o-3多不饱和脂肪酸(DHA和EPA)含量检测结果。模型rval2,分别为0.9625和0.9674；可见-短波近红外光谱、长波近红外光谱和和中红外光谱最优检测模型对藻油DHA含量检测的屹7分别为0.9190、0.9232和0.8748,对EPA含量检测的吃,最大值分别为o.9213、0.8757和0.8857,但均未能达到核磁共振技术的检测精度；基于可见光激光源的拉曼光谱技术不能准确测量藻油DHA和EPA含量。其模型RMSECV分别高达21.2707和1.8529,是核磁共振谱模型RMSECV值的1.67和2.68倍；特征变量选择能有效提高藻油DHA和EPA含量检测精度,模型RMSECV平均值能够降低18.70%和29.03%；10个藻油品质指标检测模型中有9个的最佳特征变量选择算法为UVE结合SPA,说明UVE可以有效提高SPA在藻油品质指标检测过程中特征变量提取的效率。
(4)研究建立了藻粉品质信息快速获取方法与模型。研究了藻粉类别的可见-近红外光谱快速检测方法。基于7个特征波长的SPA-LS-SVM模型分类正确率达到100%。研究了藻粉蛋白质含量的可见-近红外光谱快速检测方法。UVE-SPA-MLR为最佳检测模型,其中rpre2、RMSEP和RPD分别为0.9750、0.2344和6.2206,检测精度能够满足实际要求；研究了藻粉辐照剂量的光谱快速检测方法。UVE-SPA-BP-ANN模型为最优的藻粉辐照剂量检测模型,其中rpre2、RMSEP和RPD分别为0.9850、0.6414和8.1366,检测精度能够满足实际要求；研究了藻粉掺假信息的光谱快速检测方法。DVA分析结果表明短波近红外光谱比全波段光谱更适合被用于藻粉掺假信息的快速获取,而LS-SVM算法要优于PLS和PLS2算法。对于单一掺假物、两组掺假物和多组掺假物情况下面粉和绿豆粉LS-SVM短波近红外光谱检测模型的rpre2分别为0.9966、0.9430和0.9740,以及0.9903、0.9474和0.9705。
Microalgae have broad application prospects in many fields such as fuel, energy, food industry, biotechnology, pharmaceutical industry, animal feed, environmental monitoring, and pollution control. Microalgae have excellent qualities of fast growth and reproduction, high photosynthetic efficiency, short doubling time, high production per acre, low greenhouse gas emission and little or no competition with food production. Because of the growing threats of energy, food and environmental issues, microalgal biorescoure industry has gained more attention worldwide. At present, one of the key questions is how to improve the growing efficiency, increase the value of algal products and reduce the production cost. This thesis proposed a new concept of DIGITAL MICROALGAE, which means using high-tech methods to rapidly and non-invasively measure the microalgal growth information and qualities of algal products in the processes of microalgal breeding, cultivation, harvesting, processing and sales. The obtained information can be used for the optimal decision-making control and system operation, and ultimately optimizes the production and management of microalgae, and improves production efficiency, ensures the quality of products, reduces costs and increases economic benefits. The foundation and key in the process of Digital Microalgae is the rapid and non-invasive determination of microalgal growth information and qualities of algal products. Digital Microalgae will become the research priorities and hot spots of the world. At present, however, conventional off-line analyses are rather time-consuming and inefficient which can not meet the needs of modern production of microalgal biorescoure industry.
In this work, according to the requirements of relevant quality information of microalgal in the processes of breeding, cultivation, harvesting, processing and sales, microscopy imaging technique, spectral analysis technique, hyperspectral imaging technique, nuclear magnetic resonance (NMR) technique combined with image process algorithms and chemometrics were used for rapid and non-invasive determination of comprehensive quality information of microalgal such as microscopic morphological features, growth information, qualities of algal oils and algal powders to further provide information support for the high efficient production and system optimization of microalgal biorescoure industry. The main research contents and results are shown as follow:
(1) Microscopy image processing algorithms were proposed to rapid measure morphological features of Spirulina microalga filaments. After the image preprocessing, binary images of each filament at vertical position were obtained. The filament rotation was applied to make the filament characteristic parameters extraction easier. The vertices of filaments were determined based on discrete contour evolution (DCE) algorithm. Because traditional algorithm of skeleton extraction has the disadvantage of bringing in branches, a new algorithm was proposed to reduce the branches and obtain the Spirulina length based on the main skeleton. Morphological features such as length, diameter of helix, degree of spiralization, width of filament, pitch, helix number, and helix pitch were obtained based on the vertices of filaments. The errors between manually obtained and automatic calculated values were 4.7%for degree of spiralization,5.6%for diameter of helix and 6.2%for width of filament, respectively. Furthermore, a Spirulina Morphological Feature Extraction System Software was developed. The feature extraction time is about 30 seconds and the measurement accuracy was 99%by using the software, while manual measurement usually takes five minutes with the accuracy of 93%.
(2) Spectroscopy and hyperspectral imaging techniques were for the first time applied to establish the quantitative relationship models between the spectra and hyperspectral image information and algal growing information respectively and realized the rapid determination of algal growing information. In the spectral analysis, transmittance model and transflectance model were better than reflectance model and reflectance measured outside the bioreactor model. The best coefficients of determination of prediction (rpre2e)values of the models were 0.9836,0.9777 and 0.9487 for the spectral analysis of the dry weight, lipid content per unit volume and lipid content per unit weight. There were average 9.41 variables for the spectral efficient variable models. Compared to the whole spectral models,99.62%of the variables were eliminated, while the average rpre2 values of the spectral efficient variable models only decreased 4.19%. In all the twelve spectral efficient variable models, for eight models, uninformative variable elimination combined with successive projections algorithm (UVE-SPA) performed better than using SPA directly in the variable selection processes. It shows that UVE could effectively improve the variable selection accuracy of SPA. The rpre2 values of the hyperspectral image models for the measurement of dry weight, chlorophyll content per unit volume, chlorophyll content per unit weight, chlorophyll a content per unit volume, chlorophyll a content per unit weight, chlorophyll b content per unit volume and chlorophyll b content per unit weight were 0.9891,0.9882,0.9242,0.9895,0.9444, and 0.9780 respectively. There were avaerage 7.41 variables for the hyperspectral image efficient variable models. Compared to the whole spectral models,99.7% variables were eliminated, while the average rpre2 value of the spectral efficient variable models was 0.9550 (0.9573 for the whole spectral model). In all the seven hyperspectral image efficient variable models, for five models, UVE-SPA was better than using SPA directly in the variable selection processes. It shows that UVE could improve the hyperspectral image variable selection accuracy compared to using SPA directly. Moreover, the quality distribution maps of microalgal slurry were obtained based on the algal hyperspectral images. The results show that hyperspectral imaging technique is better than RGB images to measure the growing information of algae.
(3) Rapid determination method and system of the content of w-3 PUFAs in algal oil were proposed. NMR obtained the best DHA and EPA prediction models. The best coefficients of determination of validation (rpre2) values of two models were 0.9625 and 0.9674 respectively. The best rpre2, values of visible and short-wave near infrared spectral model, long-wave near infrared spectral model, and mid-infrared spectral model were 0.9790,0.9232 and 0.8748 for DHA analysis,0.9213,0.8757 and 0.8857 for EPA analysis respectively. However their results were not as good as those of NMR models. The Raman spectroscopy with 514 nm light laser didn't perform well for DHA and EPA prediction. The RMSECV of their models were 21.2707 and 1.8529, which were 1.67 and 2.68 times of those of NMR models. The effective variable selection can improve the DHA and EPA prediction accuracy. Models'average RMSECV values decreased 18.70%and 29.03%respectively. In all the ten efficient variable models, for nine models, UVE-SPA was better than using SPA directly in the variable selection processes. It shows that UVE could improve the variable selection accuracy compared to using SPA directly in the algal oil analysis.
(4) Rapid determination method and models of the quality of algal powders were established. Based on visible and near infrared spectroscopy, SPA-least square support vector machine (SPA-LS-SVM) with seven effective variables reached 100%correct answer rate for the algal powder classification. Visible and near infrared spectroscopy was used to predict the protein content in algal powders. UVE-SPA-multiple linear regression (UVE-SPA-LS-SVM) obtained the best result with the rpre2, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9750,0.2344, and 6.2206, respectively. The detection accuracy can meet the practical requirements. Spectroscopy technique was used to determine the irradiation dose of algal powders. UVE-SPA-Back-Propagation Artificial Neural Network (UVE-SPA-BP-ANN) obtained the best result with the rpre2, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9850,0.6414, and 8.1366, respectively. The detection accuracy can meet the practical requirements. Visible and near infrared spectroscopy was used to predict the adulterant contents in algal powder. The design value analysis (DVA) indicated that for quantification of adulterants in algal powder, short-wave near infrared spectroscopy outweighs full spectra, and LS-SVM models outweigh PLS and PLS2 models. The rpre2 values of LS-SVM models based on NIR spectra were 0.9966,0.9430, and 0.9740 for flour detection and 0.9903,0.9474, and 0.9705 for mung-bean detection, while single adulterant, two adulterants and multiple adulterants were considered.
引文
1. Alonso-Salces R.M., Moreno-Rojas J.M., Holland M.V., et al. Virgin Olive Oil Authentication by Multivariate Analyses of H-1 NMR Fingerprints and delta C-13 and delta H-2 Data [J]. Journal of Agricultural and Food Chemistry,2010,58(9):5586-5596.
2. Arnold S.A., Harvey L.M., McNeil B., et al. Employing near-infrared spectroscopic methods and analysis for fermentation monitoring and control-Part 1, method development [J]. Biopharm International-the Applied Technologies of Biopharmaceutical Development,2002,15(11):26-34.
3. Arnold S.A., Harvey L.M., McNeil B., et al. Employing near-infrared spectroscopic methods of analysis for fermentation monitoring and control-Part 2, implementation strategies [J]. Biopharm International-the Applied Technologies of Biopharmaceutical Development,2003,16(1):47-49,70s.
4. Avigad V. Strain selection of Spirulira suitable for mass production [J]. Hydrobiologia,1987,151(1):75-77.
5. Azizian H., Kramer J.K.G., Ehler S., et al. Rapid quantitation of fish oil fatty acids and their ethyl esters by FT-NIR models [J]. European Journal of Lipid Science and Technology,2010,112(4):452-462.
6. Bai X., Latecki L.J., Liu W.Y. Skeleton pruning by contour partitioning with discrete curve evolution [J]. Ieee Transactions on Pattern Analysis and Machine Intelligence,2007,29(3):449-462.
7. Balasubramanya N.N. Protein efficiency ratio of Spinilina [J]. Indian Journal of Nutrition and Dietetics,1985, 21:165-167.
8. Barison A., da Silva C.W.P., Campos F.R., et al. A simple methodology for the determination of fatty acid composition in edible oils through H-I NMR spectroscopy [J]. Magnetic Resonance in Chemistry,2010,48(8): 642-650.
9. Becher E.W. Production and utilization of the blue-green alga Spirulina in India [J]. Biomass,1984,4: 105-125.
10. Benning L.G., Phoenix V.R., Yee N., et al. The dynamics of cyanobacterial silicification:An infrared micro-spectroscopic investigation [J]. Geochimica Et Cosmochimica Acta,2004,68(4):743-757.
11. Berberoglu H., Gomez P.S., Pilon L. Radiation characteristics of Botryococcus braunii, Chlorococcum littorale, and Chlorella sp. used for CO2 fixation and biofuel production [J]. Journal of Quantitative Spectroscopy & Radiative Transfer,2009,110(17):1879-1893.
12. Birch E.E., Hoffman D.R., Uauy R., et al. Visual acuity and the essentiality of docosahexaenoic acid and arachidonic acid in the diet of term infants [J]. Pediatric Research,1998,44(2):201-209.
13. Blum H. Biological Shape and Visual Science.1. [J]. Journal of Theoretical Biology,1973,38(2):205-287.
14. Bogren K., Steig S. On-line monitoring of glutamine, glutamate, glucose, ammonia, and organic acids using the BIO+monitoring system. [J]. Abstracts of Papers of the American Chemical Society,2000,219: U214-U214.
15. Brereton R.G. Introduction to multivariate calibration in analytical chemistry [J]. Analyst,2000,125(11): 2125-2154.
16. Brosnan T., Sun D.W. Improving quality inspection of food products by computer vision-a review [J]. Journal of Food Engineering,2004,61(1):3-16.
17. Cavinato A.G., Mayes D.M., Ge Z.H., et al. Noninvasive Method for Monitoring Ethanol in Fermentation Processes Using Fiberoptic near-Infrared Spectroscopy [J]. Analytical Chemistry,1990,62(18):1977-1982.
18. Cervera A.E., Petersen N., Lantz A.E., et al. Application of Near-Infrared Spectroscopy for Monitoring and Control of Cell Culture and Fermentation[J]. Biotechnology Progress,2009,25(6):1561-1581.
19. Chen X.J., Lei X.X. Application of a Hybrid Variable Selection Method for Determination of Carbohydrate Content in Soy Milk Powder Using Visible and Near Infrared Spectroscopy [J]. Journal of Agricultural and Food Chemistry,2009,57(2):334-340.
20. Cheng J.N., Ji G.R., Feng C, et al. Application of Connected Morphological Operators to Image Smoothing and Edge Detection of Algae [C]. Itcs:2009 International Conference on Information Technology and Computer Science, Proceedings, Vol 2, Proceedings,2009:73-76,631.
21. Chiovitti A., Heraud P., Dugdale T.M., et al. Divalent cations stabilize the aggregation of sulfated glycoproteins in the adhesive nanofibers of the biofouling diatom Toxarium undulatum [J]. Soft Matter,2008, 4(4):811-820.
22. Chiu S.Y., Kao C.Y., Tsai M.T., et al. Lipid accumulation and CO2 utilization of Nannochloropsis oculata in response to CO2 aeration [J]. Bioresource Technology,2009,100(2):833-838.
23. Ciferri O. Spirulina, the edible microorganism [J]. Microbiology Reviews,1983,47(4):551-578.
24. Ciferri O., Tiboni O. The biochemistry and industrial potential of Spirulina [J]. Annual Review of Microbiology,1985,39:503-526.
25. Converti A., Casazza A.A., Ortiz E.Y., et al. Effect of temperature and nitrogen concentration on the growth and lipid content of Nannochloropsis oculata and Chlorella vulgaris for biodiesel production [J]. Chemical Engineering and Processing,2009,48(6):1146-1151.
26. Coutinho P., Rema P., Otero A., et al. Use of biomass of the marine microalga Isochrysis galbana in the nutrition of goldfish (Carassius auratus) larvae as source of protein and vitamins [J]. Aquaculture Research, 2006,37(8):793-798.
27. Cozzolino D., Murray I., Chree A., et al. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy [J]. Lwt-Food Science and Technology, 2005,38(8):821-828.
28. Crowley J., Arnold S.A., Wood N., et al. Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy [J]. Enzyme and Microbial Technology,2005,36(5-6):621-628.
29. Cuello J.L. Engineering Education in the 21st Century:Toward an Innovation-Driven and Sustainability-Centered Engineering Design Proceedings of the 4th Department of Science and Technology Engineering Research & Development for Technology (ERDT) Conference "Charting the R&D Roadmap of the Philippines.2010.
30. Dean A.P., Martin M.C., Sigee D.C. Resolution of codominant phytoplankton species in a eutrophic lake using synchrotron-based Fourier transform infrared spectroscopy [J]. Phycologia,2007,46(2):151-159.
31. Di Ruberto C, Dempster A., Khan S., et al. Analysis of infected blood cell images using morphological operators [J]. Image and Vision Computing,2002,20(2):133-146.
32. Du C.J., Sun D.W. Learning techniques used in computer vision for food quality evaluation:a review [J]. Journal of Food Engineering,2006,72(1):39-55.
33. Duffy J.E., Canuel E.A., Adey W., et al. Biofuels:Algae [J]. Science,2009,326(5958):1345-1345.
34. El-Abassy R.M., Donfack P., Materny A. Assessment of conventional and microwave heating induced degradation of carotenoids in olive oil by VIS Raman spectroscopy and classical methods [J]. Food Research International,2010,43(3):694-700.
35. Endo Y., Tagiri-Endo M., Kimura K. Rapid determination of iodine value and saponification value offish oils by near-infrared spectroscopy [J]. Journal of Food Science,2005,70(2):C127-C131.
36. Eswaran K., Mairh O.P., Rao P.V.S. Inhibition of pigments and phycocolloid in a marine red alga Gracilaria edulis by ultraviolet-B radiation [J]. Biologia Plantarum,2002,45(1):157-159.
37. Galtier O., Dupuy N., Le Dreau Y., et al. Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra [J]. Analytica Chimica Acta,2007,595(1-2):136-144.
38. Gao K.S., Wu Y.P., Li G., et al. Solar UV radiation drives CO2 fixation in marine phytoplankton:A double-edged sword [J]. Plant Physiology,2007,144(1):54-59.
39. Gautz L.D., Kaufusi P., Jackson M.C., et al. Determination of kavalactones in dried kava (Piper methysticum) powder using near-infrared reflectance spectroscopy and partial least-squares regression [J]. Journal of Agricultural and Food Chemistry,2006,54(17):6147-6152.
40. Ge Z.H., Cavinato A.G., Callis J.B. Noninvasive Spectroscopy for Monitoring Cell-Density in a Fermentation Process [M]. Analytical Chemistry,1994,66(8):1354-1362.
41. Giordano M., Kansiz M., Heraud P., et al. Fourier transform infrared spectroscopy as a novel tool to investigate changes in intracellular macromolecular pools in the marine microalga Chaetoceros muellerii (Bacillariophyceae) [J]. Journal of Phycology,2001,37(2):271-279.
42. Guillen M.D., Carton I., Goicoechea E., et al. Characterization of cod liver oil by spectroscopic techniques. New approaches for the determination of compositional parameters, acyl groups, and cholesterol from H-1 nuclear magnetic resonance and Fourier transform infrared spectral data [J]. Journal of Agricultural and Food Chemistry,2008,56(19):9072-9079.
43. Haag A.L. Algae bloom again [J]. Nature,2007,447(7144):520-521.
44. Haddad A.M.G., Ormond R.F.G. Use of Photography and Image-Analysis for in-Situ Monitoring of Growth-Rates of Intertidal Fucoid Algae [J]. Journal of the Marine Biological Association of the United Kingdom,1994,74(3):727-730.
45. Hansen K.F., Lauritsen F.R., Degn H. An Online Sampling System for Fermentation Monitoring Using Membrane Inlet Mass-Spectrometry (Mims)-Application to Phenoxyacetic Acid Monitoring in Penicillin Fermentation [J]. Biotechnology and Bioengineering,1994,44(3):347-353.
46. Harms P., Kostov Y., Rao G. Bioprocess monitoring [J]. Current Opinion in Biotechnology,2002,13(2): 124-127.
47. Helland I.S., Naes T., Isaksson T. Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data [J]. Chemometrics and Intelligent Laboratory Systems,1995,29(2): 233-241.
48. Heraud P., Wood B.R., Tobin M.J., et al. Mapping of nutrient-induced biochemical changes in living algal cells using synchrotron infrared microspectroscopy [J]. Ferns Microbiology Letters,2005,249(2):219-225.
49. Heredia-Arroyo T., Wei W., Hu B. Oil Accumulation via Heterotrophic/Mixotrophic Chlorella protothecoides [J]. Applied Biochemistry and Biotechnology,2010,162(7):1978-1995.
50. Hongsthong A., Sirijuntarut M., Prommeenate P., et al. Revealing differentially expressed proteins in two morphological forms of Spirulina platensis by proteomic analysis [J]. Molecular Biotechnology,2007,36(2): 123-130.
51. Hopfner T., Bluma A., Rudolph G., et al. A review of non-invasive optical-based image analysis systems for continuous bioprocess monitoring [J]. Bioprocess and Biosystems Engineering,2010,33(2):247-256.
52. Huang Y.L., Foellmer T.J., Ang K.C., et al. Characterization and application of an on-line flow injection analysis wall-jet electrode system for glucose monitoring during fermentation [J]. Analytica Chimica Acta, 1995,317(1-3):223-232.
53. Isaksson T., Naes T. The Effect of Multiplicative Scatter Correction (Msc) and Linearity Improvement in NIR Spectroscopy [J]. Applied Spectroscopy,1988,42(7):1273-1284.
54. Israel A., Martinez-Goss M., Friedlander M. Effect of salinity and pH on growth and agar yield of Gracilaria tenuistipitata var. liui in laboratory and outdoor cultivation [J]. Journal of Applied Phycology,1999,11(6): 543-549.
55. Jeeji Bai N. Competitive exclusion or morphological transformation?A case study with Spirulina fusiformis [J]. Archives of Hydrobiology,1985,71:191-199.
56. Jeeji Bai N., Seshadri C.V. On coiling and uncoiling of trichromes in the genus Spirulina [J]. Archives of Hydrobiology,1980,60:32-47.
57. Keefe P.D., Draper S. An automated machine vision system for the morphometric of new cultivars and plant genebank accessions [J]. Plant Varieties and Seeds,1998,1:1-11.
58. Khanmohammadi M., Garmarudi A.B., Ghasemi K., et al. Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry [J]. Microchemical Journal,2009,91(1): 47-52.
59. Lankmayr E., Mocak J., Serdt K., et al. Chemometricat classification of pumpkin seed oils using UV-Vis, NIR and FTIR spectra [J]. Journal of Biochemical and Biophysical Methods,2004,61(1-2):95-106.
60. Lasko D.R., Wang D.I.C. On-line monitoring of intracellular ATP concentration in Escherichia coli fermentations [J]. Biotechnology and Bioengineering,1996,52(3):364-372.
61. Latecki L.J., Lakamper R. Convexity rule for shape decomposition based on discrete contour evolution [J]. Computer Vision and Image Understanding,1999,73(3):441-454.
62. Latecki L.J., Lakamper R. Shape similarity measure based on correspondence of visual parts[J]. Ieee Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1185-1190.
63. Lee J.H., Choung M.G. Determination of protein content in pea by near infrared spectroscopy [J]. Food Science and Biotechnology,2009,18(1):60-65.
64. Lee S.M., Kim S., Park J.B., et al. Prediction of chemical compositions for on-line quality measurement of red pepper powder using near infrared reflectance Spectroscopy (NIRS) [J]. Food Science and Biotechnology, 2005,14(2):280-285.
65. Lewin R.A. Uncoiled variants of Spirulina platensis (Cyanophyceae:Oscillatoriaceae) [J]. Algological Studies, 1980,26:48-52.
66. Li P.L., Miao X.L., Li R.X., et al. In Situ Biodiesel Production from Fast-Growing and High Oil Content Chlorella pyrenoidosa in Rice Straw Hydrolysate [J]. Journal of Biomedicine and Biotechnology,2011:
67. Liu B.P., Qin H.J., Luo X., et al. Determination of four contents of feedstuff powder using near infrared Spectroscopy by PLS-BP model [J]. Spectroscopy and Spectral Analysis,2007,27(10):2005-2009.
68. Ma Q., Hao G.Q., Qiao Y.J., et al. Determination of the artificial bezoar powder in bezoar powder by near-infrared spectrometry and support vector machine [J]. Spectroscopy and Spectral Analysis,2006,26(10): 1842-1845.
69. Macaloney G., Hall J.W., Rollins M.J., et al. The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant Escherichia coli production process [J]. Bioprocess Engineering,1997,17(3):157-167.
70. Mairet F., Bernard O., Masci P., et al. Modelling neutral lipid production by the microalga Isochrysis aff. galbana under nitrogen limitation [J]. Bioresource Technology,2011,102(1):142-149.
71. Makrides M., Neumann M., Simmer K., et al. Are long-chain polyunsaturated fatty-acids essential nutrients in infancy [J]. Lancet,1995,345(8963):1463-1468.
72. Mannina L., D'Imperio M., Capitani D., et al. H-1 NMR-based protocol for the detection of adulterations of refined olive oil with refined hazelnut oil [J]. Journal of Agricultural and Food Chemistry,2009,57(24): 11550-11556.
73. Maraboli A., Cattaneo T.M.P., Giangiacomo R. Detection of vegetable proteins from soy, pea and wheat isolates in milk powder by near infrared spectroscopy [J]. Journal of near Infrared Spectroscopy,2002,10(1): 63-69.
74. Mauer L.J., Chernyshova A.A., Hiatt A., et al. Melamine detection in infant formula powder using near-and mid-infrared spectroscopy [J]. Journal of Agricultural and Food Chemistry,2009,57(10):3974-3980.
75. Medina A.R., Gimenez A.G., Camacho F.G., et al. Concentration and purification of stearidonic, eicosapentaenoic, and docosahexaenoic acids from cod-liver oil and the marine microalga isochrysis-galbana [J]. Journal of the American Oil Chemists Society,1995,72(5):575-583.
76. Miao X.L., Wu Q.Y. High yield bio-oil production from fast pyrolysis by metabolic controlling of Chlorella protothecoides [J]. Journal of Biotechnology,2004,110(1):85-93.
77. Min M., Lee W.S. Determination of significant wavelengths and prediction of nitrogen content for citrus [J]. Transactions of the Asae,2005,48(2):455-461.
78. Min R.W., Nielsen J., Villadsen J. On-line monitoring of glucose and penicillin by sequential injection analysis [J]. Analytica Chimica Acta,1996,320(2-3):199-205.
79. Mori T.A., Bao D.Q., Burke V., et al. Docosahexaenoic acid but not eicosapentaenoic acid lowers ambulatory blood pressure and heart rate in humans [J]. Hypertension,1999,34(2):253-260.
80. Muhling M., Harris N., Belay A., et al. Reversal of helix orientation in the cyanobacterium Arthrospira[J]. Journal of Phycology,2003,39(2):360-367.
81. Ng C.L., Wehling R.L., Cuppett S.L. Method for determining frying oil degradation by near-infrared spectroscopy[J]. Journal of Agricultural and Food Chemistry,2007,55(3):593-597.
82. O'shea S.K., Holland F., Bilodeau A. Modeling the effects of salinity and pH on the cadmium bioabsorptive properties of the microalgae isochrysis galbana (T-Iso) in coastal waters [J]. Journal of Coastal Research,2010, 26(1):59-66.
83. Ogbonda K.H., Aminigo R.E., Abu G.O. Influence of temperature and pH on biomass production and protein biosynthesis in a putative Spinlina sp. [J]. Bioresource Technology,2007,98(11):2207-2211.
84. Otsu N. A threshold selection method from Gray-level histograms [J]. IEEE Transactions on Systems, Man, and Cybernetics,1979, SMC-9(1):62-66.
85. Pencreach G., Poisson L., Soultani S., et al. Use of lipases for the production of a DHA-rich lipid fraction from the microalgae Isochrysis galbana [J]. Journal of Biotechnology,2007,131(2):S77-S78.
86. Poisson L., Ergan F. Docosahexaenoic acid ethyl esters from Isochrysis galbana[J]. Journal of Biotechnology, 2001,91(1):75-81.
87. Pope J.A., Nelson R.A., Schaffner C.P., et al. Applications of thin-layer chromatography, high-performance liquid-chromatography and mass-spectrometry in the fermentation and isolation of the antibiotic nybomycin [J]. Journal of Industrial Microbiology,1990,6(1):61-69.
88. Poppsnijders C, Schouten J.A., Heine R.J., et al. Dietary supplementation of omega-3 polyunsaturated fatty-acids improves insulin sensitivity in non-insulin-dependent diabetes [J]. Netherlands Journal of Medicine, 1986,29(2):69-69.
89. Rebolloso-Fuentes M.M., Navarro-Perez A., Garcia-Camacho F., et al. Biomass nutrient profiles of the microalga Nannochloropsis [J]. Journal of Agricultural and Food Chemistry,2001,49(6):2966-2972.
90. Ren G.X., Chen F. Simultaneous quantification of ginsenosides in American ginseng (Panax quinquefolium) root powder by visible/near-infrared reflectance spectroscopy [J]. Journal of Agricultural and Food Chemistry, 1999,47(7):2771-2775.
91. Ribeiro E., Stafslien S.J., Casse F., et al. Automated image-based method for laboratory screening of coating libraries for adhesion of algae and bacterial biofilms [J]. Journal of Combinatorial Chemistry,2008,10(4): 586-594.
92. Ross N.E., Pritchard C.J., Rubin D.M., et al. Automated image processing method for the diagnosis and classification of malaria on thin blood smears [J]. Medical & Biological Engineering & Computing,2006, 44(5):427-436.
93. Roychoudhury P., Harvey L.M., McNeil B. At-line monitoring of ammonium, glucose, methyl oleate and biomass in a complex antibiotic fermentation process using attenuated total reflectance-mid-infrared (ATR-MTR) spectroscopy [J]. Analytica Chimica Acta,2006,561(1-2):218-224.
94. Saleh M.I., Meullenet J.F., Siebenmorgen T.J. Development and Validation of Prediction Models for Rice Surface Lipid Content and Color Parameters Using Near-Infrared Spectroscopy:A Basis for Predicting Rice Degree of Milling [J]. Cereal Chemistry,2008,85(6):787-791.
95. Saoudihelis L., Dubacq J.P., Marty Y., et al. Influence of growth-rate on pigment and lipid-composition of the microalga isochrysis aff galbana clone t-iso [J]. Journal of Applied Phycology,1994,6(3):315-322.
96. Sato T. Nondestructive measurements of lipid content and fatty acid composition in rapeseeds (Brassica napus L.) by near infrared spectroscopy [J]. Plant Production Science,2008,11(1):146-150.
97. Satyarthi J.K., Srinivas D., Ratnasamy P. Estimation of free fatty acid content in oils, fats, and biodiesel by H-1 NMR spectroscopy [J]. Energy & Fuels,2009,23:2273-2277.
98. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures [J]. Analytical Chemistry,1964,36(8):1627-1639.
99. Sheridan C. Big oil bucks for algae [J]. Nature Biotechnology,2009,27:783.
100. Shimamatsu H. A pond for edible Spirulina production and its hydraulic studies [J]. Hydrobiology,1987,151: 83-89.
101. Silveira F.L., Silveira L., Villaverde A.B., et al. Use of dispersive raman spectroscopy in the determination of unsaturated fat in commercial edible oil-and fat-containing industrialized foods [J]. Instrumentation Science & Technology,2010,38(1):107-123.
102. Sinija V.R., Mishra H.N. FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules [J]. Lwt-Food Science and Technology,2009,42(5):998-1002.
103. Siscovick D.S., Raghunathan T.E., King I., et al. Dietary-intake and cell-membrane levels of long-chain n-3 polyunsaturated fatty-acids and the risk of primary cardiac-arrest [J]. Jama-Journal of the American Medical Association,1995,274(17):1363-1367.
104. Spolaore P., Joannis-Cassan C, Duran E., et al. Commercial applications of microalgae [J]. Journal of Bioscience and Bioengineering,2006,101(2):87-96.
105. Srinivasan N., Kasthurikrishnan N., Cooks R.G., et al. Online monitoring with feedback-control of bioreactors using a high ethanol tolerance yeast by membrane introduction mass-spectrometry [J]. Analytica Chimica Acta,1995,316(2):269-276.
106. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999,9(3):293-300.
107. Thiel S., Wiltshire R.J. The automated detection of cyanobacteria using digital image-processing techniques [71. Environment International,1995,21(2):233-236.
108. Tzovenis I., DePauw N., Sorgeloos P. Effect of different light regimes on the docosahexaenoic acid (DHA) content of Isochrysis-Aff-galbana (clone T-ISO) [J]. Aquaculture International,1997,5(6):489-507.
109. Umdu E.S., Tuncer M., Seker E. Transesterification of Nannochloropsis oculata microalga's lipid to biodiesel on AL2O3 supported CaO and MgO catalysts [J]. Bioresource Technology,2009,100(11):2828-2831.
110. Vaccari G., Dosi E., Campi A.L., et al. A near-Infrared spectroscopy technique for the control of fermentation processes-an application to lactic-acid fermentation [J]. Biotechnology and Bioengineering,1994,43(10): 913-917.
111. Valenzuela-Espinoza E., Millan-Nunez R., Nunez-Cebrero F. Protein, carbohydrate, lipid and chlorophyll alpha content in Isochrysis aff. galbana (clone T-Iso) cultured with a low cost alternative to the f/2 medium [J]. Aquacultural Engineering,2002,25(4):207-216.
112. van Bergeijk S.A., Salas-Leiton E., Canavate J.P. Low and variable productivity and low efficiency of mass cultures of the haptophyte Isochrysis aff. galbana (T-iso) in outdoor tubular photobioreactors [J]. Aquacultural Engineering,2010,43(1):14-23.
113. van der Heijden G.W.A.M., Vossepoel A.M., Polder G. Measuring onion cultivars with image analysis using inflection points[J]. Euphytica,1996,87(1):19-31.
114. van Eck J.W., van der Heijden G.W.A.M., Polder G. Accurate measurement of size and shape of cucumber fruits with image analysis Journal of Agricultural Engineering Research,1998,70(4):335-343.
115. van Eykelenburg C. The ultrastructure of Spirulina platensis in relation to temperature and light intensity [J]. Antonie Van Leeuwenhoek,1979,45(3):369-390.
116. Vapnik V.N. Statistical learning theory [M]. New York John Wiley and Sons Inc.,1998.
117. Vardy S., Uwins P. Fourier transform infrared microspectroscopy as a tool to differentiate Nitzschia closterium and Nitzschia longissima [J]. Applied Spectroscopy,2002,56(12):1545-1548.
118. Volkova S.E., Ilyasova N.Y., Ustinov A.V., et al. Methods for analyzing the images of blood preparations [J]. Optics and Laser Technology,1995.27(4):255-261.
119. Vonshak A. Spirulina platensis (Arthrospira):Physiology, Cell-biology and Biotechnology [M]. London Taylor & Francis,1997.
120. Wang L., Lee F.S.C., Wang X.R., et al. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR[J]. Food Chemistry,2006,95(3):529-536.
121. Wang Z.P., Chen S.M., Jia X.M., et al. The effect of environmental factors and gamma-rays on the morphology and growth of Spirulina platensis [J]. The Journal of Zhejiang Agricultural University,1997,23: 36-40.
122. Wang Z.P., Zhao Y. Morphological reversion of Spirulina (Arthrospira) platensis (Cyanophyta):From linear to helical [J]. Journal of Phycology,2005,41(3):622-628.
123. Werman M.J., Sukenik A., Mokady S. Effects of the marine unicellular alga Nannochloropsis sp to reduce the plasma and liver cholesterol levels in male rats fed on diets with cholesterol [J]. Bioscience Biotechnology and Biochemistry,2003,67(10):2266-2268.
124. White S.F., Tothill I.E., Newman J.D., et al. Development of a mass-producible glucose biosensor and flow-injection analysis system suitable for on-line monitoring during fermentations [J]. Analytica Chimica Acta,1996,321(2-3):165-172.
125. Williams P.C. Near-Infrared Technology in the Agricultural and Food Industries [M]. Saint Paul, MN, USA American Association of Cereal Chemists,2001.
126. Wu D., Cao F., Feng S.J., et al. Application of infrared spectroscopy technique to protein content fast measurement in milk powder based on support vector machines [J]. Spectroscopy and Spectral Analysis, 2008a,28(5):1071-1075.
127. Wu D., Cao F., Zhang H., et al. Study on disease level classification of rice panicle blast based on visible and near infrared spectroscopy [J]. Spectroscopy and Spectral Analysis,2009a,29(12):3295-3299.
128. Wu D., Chen X.J., Shi P.Y., et al. Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination [J]. Analytica Chimica Acta,2009b,634(2):166-171.
129. Wu D., Feng S., He Y. Infrared spectroscopy technique for the nondestructive measurement of fat content in milk powder [J]. Journal of Dairy Science,2007,90(8):3613-3619.
130. Wu D., Feng S., He Y. Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis [J]. Journal of Dairy Science,2008b,91(3):939-949.
131. Wu D., He Y., Feng S. Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment [J]. Analytica Chimica Acta,2008c,610(2):232-242.
132. Wu D., He Y., Feng S.J., et al. Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM [J]. Journal of Food Engineering,2008d,84(1):124-131.
133. Wu D., He Y., Nie P.C., et al. Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice [J]. Analytica Chimica Acta,2010,659(1-2):229-237..
134. Wu D., He Y., Shi J.H., et al. Exploring Near and Midinfrared Spectroscopy to Predict Trace Iron and Zinc Contents in Powdered Milk [J]. Journal of Agricultural and Food Chemistry,2009c,57(5):1697-1704.
135. Wu D., Jin C.H., He Y. Study on Combinatorial Optimization of Spectral Principal Components Using Successive Projections Algorithm [J]. Spectroscopy and Spectral Analysis.2009d,29(10):2734-2737.
136. Wu D., Nie P.C., Cuello J., et al. Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder [J]. Journal of Food Engineering, 2011,102(3):278-286.
137. Wu D., Wu H.X., Cai J.B., et al. Classifying the Species of Exopalaemon by Using Visible and near Infrared Spectra with Uninformative Variable Elimination and Successive Projections Algorithm [J]. Journal of Infrared and Millimeter Waves,2009e,28(6):423-427.
138. Wu D., Yang H.Q., Chen X.J., et al. Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine [J]. Journal of Food Engineering,2008e,88(4): 474-483.
139. Wu H.Y., Gao K.S., Villafane V.E., et al. Effects of solar UV radiation on morphology and photosynthesis of filamentous cyanobacterium Arthrospira platensis [J]. Applied and Environmental Microbiology,2005,71(9): 5004-5013.
140. Xu L., Ji G.R., Wang N.Q., et al. An approach for image compression of algae cell using multiwavelets [J]. Advanced Intelligent Computing Theories and Applications,2007.2:836-843,1375.
141. Yu C.C.. Chen H.W., Chen M.J., et al. Chemical Composition and Bioactivities of the Marine Alga Isochrysis galbana from Taiwan [J]. Natural Product Communications,2010,5(12):1941-1944.
142. Zou X.B., Zhao J.W., Huang X.Y., et al. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of'Fuji'apple based on different PLS models [J]. Chemometrics and Intelligent Laboratory Systems,2007,87(1):43-51.
143.蔡隽,鲍旭东,吴磊,et al基于活动轮廓模型的彩色白细胞图像自动分割方法研究[J].生物医学工程研究,2005,(04)：218-222.
144.曹红宝.基于双光谱有毒赤潮优势藻图像分析的研究[D].天津大学,2005.
145.陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用[J].分析测试学报,2007,(01)：66-69.
146.单杨,朱向荣,许青松,et al近红外光谱结合小波变换-径向基神经网络用于奶粉蛋白质与脂肪含量的测定[J].红外与毫米波学报,2010,(02)：128-131.
147.邓月娥,周群,孙素琴.FTIR光谱法与奶粉的品质分析[J].光谱学与光谱分析,2005,(12)：1972-1974.
148.杜聪,王世新,周艺, et al利用Hyperion高光谱数据的三波段法反演太湖叶绿素a浓度[J].环境科学,2009,(10)：2904-2910.
149.顾小红,任璐,陈尚卫, et al.核磁共振技术在食品研究中的应用[J].食品工业科技,2005,(09)：189-191+194.
150.韩冰,周虹丽,朱建华, et al.实验室混合藻种的光谱分解方法研究[J].海洋技术,2009,(04)：58-62.
151.韩锦华,王雪青,苗惠.三种海洋微藻抗肿瘤活性的研究[J].天津科技大学学报,2006,(04)：25-28.
152.何汝洪.大螺旋藻的形态观察和培养试验[J].水生生物学报,1987,(04)：377-380.
153.胡锅亮,吕秀阳,吴建国, et al.近红外漫反射光谱法直接测定银杏叶提取物粉末中总黄酮的含量[J]药物分析杂志,2004,(0])：18-20.
154.胡鸿钧.国外螺旋藻生物技术的现状及发展趋势[J].武汉植物学研究,1997,(04)：369-374.
155.胡开辉,汪世华.小球藻的研究开发进展[J].武汉工业学院学报,2005,(03)：27-30.
156.黄振华.湛江等鞭金藻有机碳营养生态位研究[D].暨南大学,2007.
157.冀克俭,刘元俊,张银生,et al.鱼油多烯脂肪酸乙酯的NMR表征[J].化学分析计量,2001,(03)：3-5.
158.姜悦.乳制品中的DHA：微藻油还是鱼油?[J].食品工业科技,2006,(12)：191-192.
159.蒋汉明,高坤山.两种海洋微藻脂肪酸组成研究[J].科学技术与工程,2003,(01)：46-48.
160.金华丽,卞科.近红外光谱法检测小麦粉中的水分含量[J].中国粮油学报,2010a,(08)：109-112.
161.金华丽,王金水.近红外光谱法检测小麦粉中灰分含量的研究[J].河南工业大学学报(自然科学版),2010b,(01)：14·17,21.
162.李家旺,司民真.傅里叶变换红外光谱法研究核辐照对螺旋藻粉成分的影响[J].光谱实验室,2009,(04)：888-892.
163.李晋楠,汪志平. RAPD分子标记技术用于螺旋藻(Spirulina)分类的研究[J].海洋与湖沼,2002,(02)：203－208.
164.李神州,李岳,陶明方.浅谈螺旋藻的开发价值及应用[J].科技资讯,2008,(14)：202+204.
165.李文权,王清池,陈清花,et al.超声波对球等鞭金藻脂肪酸组成的效应研究[J].海洋科学,2000,(04)：7－9.
166.李秀波,徐旭东,孔任秋.五种微绿球藻产油和产多不饱和脂肪酸的研究[J].水生生物学报,2010,(05)：893－897.
167.粱燕茹,李文权,刘四光.自养小球藻硫酸多糖的红外光谱分析[J].海洋科学,2007,(11);1－4.
168.刘汉元全.积极开展微藻研究确保我国长远粮食安全.人民政协报,(2010).p.App.B03.
169.刘惠芳.螺旋藻在饲料工业中的应用[J].动物科学与动物医学,2001,(02)：62-65.
170.刘世龙.红外光谱法对玉米粉和乙醇中水分含量的检测研究[D].长春理工大学,2010.
171.刘世名.小球藻(Chlorella Vulgaris)高密度异养培养[D].华南理工大学,1999.
172.陆新泉,李宁,陈世福.形态、颜色特征及神经网络在肺癌细胞识别中的应用研究[J].计算机辅助设计与图形学学报,2001,(01)：87-92.
173.罗立民,杨晓敏.白细胞图像的计算机自动分割——白细胞的自动分类研究[J].北京生物医学工程,1991,(04)：195-201.
174.马本学,应义斌,饶秀勤, et al高光谱成像在水果内部品质无损检测中的研究进展[J].光谱学与光谱分析,2009,(06)：161]-1615.
175.马增岭.阳光辐射变化对经济蓝藻螺旋藻形态、光合作用及生长的影响[D].汕头大学,2008.
176.尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理算法的作用及其发展[J].药物分析杂志,2008,(05)：824-829.
177.齐金峰,熊华,陈振林,et al.海藻油微胶囊中DHA检测预处理算法的比较[J].食品研究与开发,2009,(07)：13·17.
178.齐沛沛,王飞.制备生物柴油用小球藻的油脂富集培养研究[J].现代化工,2008,(S2)：36-39+41.
179.秦松,吴沿友,陈娟,et al显微彩色图像处理在藻类识别统计中的应用[J].农机化研究,2006,(06)：199-201+203.
180.邱江,叶勤,张嗣良.红外光谱技术在生物过程监测中的应用[J].生物工程学报,1998,(01).
181.松文.海藻生产生物柴油的潜力和前景[J].精细化工原料及中间体,2008,(12)：21.23,20.
182.苏敏,王丹红,郑能雄.近红外漫反射光谱法快速测定肉骨粉的成分[J].医学动物防制,2004,(10)：602-604.
183.孙勇.基于双光谱有毒赤潮藻图像自动识别系统的研究[D].天津大学,2007.
184.唐启义,冯明光.实用统计分析及其DPS数据处理系统.北京科学出版社,2002.
185.唐赢中,黄利群,严国安.藻类产烃研究进展[J].生物工程进展,1997,(02).
186.唐玉莲,梁逸曾,范伟,et al.应用近红外光谱快速鉴别不同年龄段人食用的奶粉品种[J].红外,2010,(01)：30-35.
187.田国辉,陈亚杰,冯清茂.拉曼光谱的发展及应用[J].化学工程师,2008,(01)：34-36.
188.汪志平,陈声明,贾小明,et al.环境因子和Y—射线对钝顶螺旋藻形态及生长的影响[J].浙江农业大学学报,1997,(01).
189.汪志平,贾小明,傅俊杰, et al不同形态钝顶螺旋藻藻丝体分离和纯化的方法[J].浙江农业学报,1998,(05).
190.汪志平.蛋白质SDS-PAGE用于螺旋藻分类及突变体鉴定的研究[J].浙江大学学报(农业与生命科学版),2000,(06).
191.王翠红,李日强,辛晓芸.钝项螺旋藻处理氨氮废水的研究[J].上海环境科学,2002,(12)：728－731+754.
192.王军锋,马金才,徐斌,et al.酿酒废水培养钝顶螺旋藻(S.platensis)与营养物质去除的研究[J].城市环境与城市生态,2002,(05)：42-44.
193.王雷,乔晓艳,董有尔,et al高光谱图像技术在农产品检测中的应用进展[J].应用光学,2009,(04)：639-645.
194.王以斌.梁强,何碧娟, et al.螺旋藻多糖酶解辅助超声波提取工艺的优化[J].现代农业科技,2009,(08)：234-235.
195.韦成礼,李永健,计玲华.国内外螺旋藻的研究和开发利用现状广西农业大学学报,1994,(04).
196.翁欣欣,陆峰,王传现,et al.近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析[J].光谱学与光谱分析.2009,(12)：3283-3287.
197.吴静珠,王一鸣,张小超,et al.基于近红外的奶粉品质检测技术研究[J].光谱学与光谱分析,2007a,(09)：1735-1738.
198.吴静珠,王一鸣,张小超,et al.支持向量机-近红外光谱法用于真假奶粉的判别[J].农机化研究,2007b,(01)：155－158.
199.吴庆余,殷实,盛国英,et al.异养黄化藻类产烃研究新发现[J].中国科学(B辑化学生命科学地学),1993,(04)：423－429.
200.吴文龙,杨志娟.螺旋藻保健食品的功能因子与研究开发进展[J].食品研究与开发,2006,(02)：129-131.
201.徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,(02)：134-142.
202.杨莉,魏少华,吴建国.用近红外反射光谱法测定乳清粉的品质性状叨.乳业科学与技术,2001,(03)：22-25.
203.杨晓敏,罗立民,韦钰. AUTOMATIC CLASSIFICATION METHOD AND SYSTEM FOR LEUKOCYTE IN HUMAN PERIPHERAL BLOOD [J]. Journal of Southeast University(English Edition),1993d, (02):1-7.
204.杨晓敏,罗立民.白细胞自动分类中的特征提取和分析[J].北京生物医学工程,1992.(04)：214-220.
205.杨晓敏,罗立民.白细胞自动分类中的纹理分析方法研究[J].中国生物医学工程学报,1993b,(02)：124－129.
206.杨晓敏,罗立民.人体末梢血液中自细胞自动识别与分类的研究(英文)[J].航天医学与医学工程,1993a,(03)：190-196.
207.杨晓敏,罗立民.血液白细胞的计算机自动分类研究明.东南大学学报,1993c,(06)：106－109.
208.杨晓敏,罗立民,韦钰.人体白细胞自动分类方法与系统实现[J].计算机学报,1994a,(02).
209.杨晓敏,罗立民,韦钰.血液白细胞计算机分类中的特征提取研究应用科学学报,1994b,(02).
210.杨晓敏,罗立民,书钰.皿液白细胞图象的计算机自动分割方法研究[J].中国生物医学工程学报,1995，(01).
211.姚守拙,何风姣,聂利华.压电监测大肠杆菌生长过程中培养液参数[J].湖南大学学报(自然科学版),1995,f01).
212.于修烛,杜双奎,岳田利,et al.食用油反式脂肪酸傅里叶红外光谱重组检测技术[J].农业机械学报,2009,(01)：114-119.
213.余颖.微绿球藻的微波辐射效应及其作用机制[D].福建师范大学,2006.
214.岳跃民,王克林,张兵, et al.高光谱遥感在生态系统研究中的应用进展[J].遥感技术与应用,2008,(04)：471-478.
215.张磊.赤潮优势藻图像分析的研究[D].天津大学,2003.
216.张忠义.螺旋藻的生物学评价食品科技,1999,(05)：52-54.
217.赵静,庞其昌,马骥,et al.中药黄连黄柏混合粉末的光谱成像分析技术[J].光学学报,2010,(11)：3259-3263.
218.赵巧华,秦伯强.藻类的光谱吸收特征及其混合藻吸收系数的分离[J].环境科学学报,2008,(02)：313-318.
219.郑立立,王磊,江克铮.鱼油中二十碳五烯酸和二十二碳六烯酸的气相色谱-红外光谱分析[J].分析化学,1994,(04)：383-385.
220.周国泰.豆奶粉红外光谱法质量评价[J].青海大学学报(自然科学版),2008,(06)：40-43.
221.周希明.螺旋藻的开发利用和培养[J].中国水产,1988,6：31-33.
222.周永国,侯文龙,杨越冬,et al傅里叶变换红外光谱法同时测定奶粉中蛋白质和脂肪含量[J].理化检验(化学分册),2009,(01)：66-69,72.
2. Arnold S.A., Harvey L.M., McNeil B., et al. Employing near-infrared spectroscopic methods and analysis for fermentation monitoring and control-Part 1, method development [J]. Biopharm International-the Applied Technologies of Biopharmaceutical Development,2002,15(11):26-34.
3. Arnold S.A., Harvey L.M., McNeil B., et al. Employing near-infrared spectroscopic methods of analysis for fermentation monitoring and control-Part 2, implementation strategies [J]. Biopharm International-the Applied Technologies of Biopharmaceutical Development,2003,16(1):47-49,70s.
4. Avigad V. Strain selection of Spirulira suitable for mass production [J]. Hydrobiologia,1987,151(1):75-77.
5. Azizian H., Kramer J.K.G., Ehler S., et al. Rapid quantitation of fish oil fatty acids and their ethyl esters by FT-NIR models [J]. European Journal of Lipid Science and Technology,2010,112(4):452-462.
6. Bai X., Latecki L.J., Liu W.Y. Skeleton pruning by contour partitioning with discrete curve evolution [J]. Ieee Transactions on Pattern Analysis and Machine Intelligence,2007,29(3):449-462.
7. Balasubramanya N.N. Protein efficiency ratio of Spinilina [J]. Indian Journal of Nutrition and Dietetics,1985, 21:165-167.
8. Barison A., da Silva C.W.P., Campos F.R., et al. A simple methodology for the determination of fatty acid composition in edible oils through H-I NMR spectroscopy [J]. Magnetic Resonance in Chemistry,2010,48(8): 642-650.
9. Becher E.W. Production and utilization of the blue-green alga Spirulina in India [J]. Biomass,1984,4: 105-125.
10. Benning L.G., Phoenix V.R., Yee N., et al. The dynamics of cyanobacterial silicification:An infrared micro-spectroscopic investigation [J]. Geochimica Et Cosmochimica Acta,2004,68(4):743-757.
11. Berberoglu H., Gomez P.S., Pilon L. Radiation characteristics of Botryococcus braunii, Chlorococcum littorale, and Chlorella sp. used for CO2 fixation and biofuel production [J]. Journal of Quantitative Spectroscopy & Radiative Transfer,2009,110(17):1879-1893.
12. Birch E.E., Hoffman D.R., Uauy R., et al. Visual acuity and the essentiality of docosahexaenoic acid and arachidonic acid in the diet of term infants [J]. Pediatric Research,1998,44(2):201-209.
13. Blum H. Biological Shape and Visual Science.1. [J]. Journal of Theoretical Biology,1973,38(2):205-287.
14. Bogren K., Steig S. On-line monitoring of glutamine, glutamate, glucose, ammonia, and organic acids using the BIO+monitoring system. [J]. Abstracts of Papers of the American Chemical Society,2000,219: U214-U214.
15. Brereton R.G. Introduction to multivariate calibration in analytical chemistry [J]. Analyst,2000,125(11): 2125-2154.
16. Brosnan T., Sun D.W. Improving quality inspection of food products by computer vision-a review [J]. Journal of Food Engineering,2004,61(1):3-16.
17. Cavinato A.G., Mayes D.M., Ge Z.H., et al. Noninvasive Method for Monitoring Ethanol in Fermentation Processes Using Fiberoptic near-Infrared Spectroscopy [J]. Analytical Chemistry,1990,62(18):1977-1982.
18. Cervera A.E., Petersen N., Lantz A.E., et al. Application of Near-Infrared Spectroscopy for Monitoring and Control of Cell Culture and Fermentation[J]. Biotechnology Progress,2009,25(6):1561-1581.
19. Chen X.J., Lei X.X. Application of a Hybrid Variable Selection Method for Determination of Carbohydrate Content in Soy Milk Powder Using Visible and Near Infrared Spectroscopy [J]. Journal of Agricultural and Food Chemistry,2009,57(2):334-340.
20. Cheng J.N., Ji G.R., Feng C, et al. Application of Connected Morphological Operators to Image Smoothing and Edge Detection of Algae [C]. Itcs:2009 International Conference on Information Technology and Computer Science, Proceedings, Vol 2, Proceedings,2009:73-76,631.
21. Chiovitti A., Heraud P., Dugdale T.M., et al. Divalent cations stabilize the aggregation of sulfated glycoproteins in the adhesive nanofibers of the biofouling diatom Toxarium undulatum [J]. Soft Matter,2008, 4(4):811-820.
22. Chiu S.Y., Kao C.Y., Tsai M.T., et al. Lipid accumulation and CO2 utilization of Nannochloropsis oculata in response to CO2 aeration [J]. Bioresource Technology,2009,100(2):833-838.
23. Ciferri O. Spirulina, the edible microorganism [J]. Microbiology Reviews,1983,47(4):551-578.
24. Ciferri O., Tiboni O. The biochemistry and industrial potential of Spirulina [J]. Annual Review of Microbiology,1985,39:503-526.
25. Converti A., Casazza A.A., Ortiz E.Y., et al. Effect of temperature and nitrogen concentration on the growth and lipid content of Nannochloropsis oculata and Chlorella vulgaris for biodiesel production [J]. Chemical Engineering and Processing,2009,48(6):1146-1151.
26. Coutinho P., Rema P., Otero A., et al. Use of biomass of the marine microalga Isochrysis galbana in the nutrition of goldfish (Carassius auratus) larvae as source of protein and vitamins [J]. Aquaculture Research, 2006,37(8):793-798.
27. Cozzolino D., Murray I., Chree A., et al. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy [J]. Lwt-Food Science and Technology, 2005,38(8):821-828.
28. Crowley J., Arnold S.A., Wood N., et al. Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy [J]. Enzyme and Microbial Technology,2005,36(5-6):621-628.
29. Cuello J.L. Engineering Education in the 21st Century:Toward an Innovation-Driven and Sustainability-Centered Engineering Design Proceedings of the 4th Department of Science and Technology Engineering Research & Development for Technology (ERDT) Conference "Charting the R&D Roadmap of the Philippines.2010.
30. Dean A.P., Martin M.C., Sigee D.C. Resolution of codominant phytoplankton species in a eutrophic lake using synchrotron-based Fourier transform infrared spectroscopy [J]. Phycologia,2007,46(2):151-159.
31. Di Ruberto C, Dempster A., Khan S., et al. Analysis of infected blood cell images using morphological operators [J]. Image and Vision Computing,2002,20(2):133-146.
32. Du C.J., Sun D.W. Learning techniques used in computer vision for food quality evaluation:a review [J]. Journal of Food Engineering,2006,72(1):39-55.
33. Duffy J.E., Canuel E.A., Adey W., et al. Biofuels:Algae [J]. Science,2009,326(5958):1345-1345.
34. El-Abassy R.M., Donfack P., Materny A. Assessment of conventional and microwave heating induced degradation of carotenoids in olive oil by VIS Raman spectroscopy and classical methods [J]. Food Research International,2010,43(3):694-700.
35. Endo Y., Tagiri-Endo M., Kimura K. Rapid determination of iodine value and saponification value offish oils by near-infrared spectroscopy [J]. Journal of Food Science,2005,70(2):C127-C131.
36. Eswaran K., Mairh O.P., Rao P.V.S. Inhibition of pigments and phycocolloid in a marine red alga Gracilaria edulis by ultraviolet-B radiation [J]. Biologia Plantarum,2002,45(1):157-159.
37. Galtier O., Dupuy N., Le Dreau Y., et al. Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra [J]. Analytica Chimica Acta,2007,595(1-2):136-144.
38. Gao K.S., Wu Y.P., Li G., et al. Solar UV radiation drives CO2 fixation in marine phytoplankton:A double-edged sword [J]. Plant Physiology,2007,144(1):54-59.
39. Gautz L.D., Kaufusi P., Jackson M.C., et al. Determination of kavalactones in dried kava (Piper methysticum) powder using near-infrared reflectance spectroscopy and partial least-squares regression [J]. Journal of Agricultural and Food Chemistry,2006,54(17):6147-6152.
40. Ge Z.H., Cavinato A.G., Callis J.B. Noninvasive Spectroscopy for Monitoring Cell-Density in a Fermentation Process [M]. Analytical Chemistry,1994,66(8):1354-1362.
41. Giordano M., Kansiz M., Heraud P., et al. Fourier transform infrared spectroscopy as a novel tool to investigate changes in intracellular macromolecular pools in the marine microalga Chaetoceros muellerii (Bacillariophyceae) [J]. Journal of Phycology,2001,37(2):271-279.
42. Guillen M.D., Carton I., Goicoechea E., et al. Characterization of cod liver oil by spectroscopic techniques. New approaches for the determination of compositional parameters, acyl groups, and cholesterol from H-1 nuclear magnetic resonance and Fourier transform infrared spectral data [J]. Journal of Agricultural and Food Chemistry,2008,56(19):9072-9079.
43. Haag A.L. Algae bloom again [J]. Nature,2007,447(7144):520-521.
44. Haddad A.M.G., Ormond R.F.G. Use of Photography and Image-Analysis for in-Situ Monitoring of Growth-Rates of Intertidal Fucoid Algae [J]. Journal of the Marine Biological Association of the United Kingdom,1994,74(3):727-730.
45. Hansen K.F., Lauritsen F.R., Degn H. An Online Sampling System for Fermentation Monitoring Using Membrane Inlet Mass-Spectrometry (Mims)-Application to Phenoxyacetic Acid Monitoring in Penicillin Fermentation [J]. Biotechnology and Bioengineering,1994,44(3):347-353.
46. Harms P., Kostov Y., Rao G. Bioprocess monitoring [J]. Current Opinion in Biotechnology,2002,13(2): 124-127.
47. Helland I.S., Naes T., Isaksson T. Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data [J]. Chemometrics and Intelligent Laboratory Systems,1995,29(2): 233-241.
48. Heraud P., Wood B.R., Tobin M.J., et al. Mapping of nutrient-induced biochemical changes in living algal cells using synchrotron infrared microspectroscopy [J]. Ferns Microbiology Letters,2005,249(2):219-225.
49. Heredia-Arroyo T., Wei W., Hu B. Oil Accumulation via Heterotrophic/Mixotrophic Chlorella protothecoides [J]. Applied Biochemistry and Biotechnology,2010,162(7):1978-1995.
50. Hongsthong A., Sirijuntarut M., Prommeenate P., et al. Revealing differentially expressed proteins in two morphological forms of Spirulina platensis by proteomic analysis [J]. Molecular Biotechnology,2007,36(2): 123-130.
51. Hopfner T., Bluma A., Rudolph G., et al. A review of non-invasive optical-based image analysis systems for continuous bioprocess monitoring [J]. Bioprocess and Biosystems Engineering,2010,33(2):247-256.
52. Huang Y.L., Foellmer T.J., Ang K.C., et al. Characterization and application of an on-line flow injection analysis wall-jet electrode system for glucose monitoring during fermentation [J]. Analytica Chimica Acta, 1995,317(1-3):223-232.
53. Isaksson T., Naes T. The Effect of Multiplicative Scatter Correction (Msc) and Linearity Improvement in NIR Spectroscopy [J]. Applied Spectroscopy,1988,42(7):1273-1284.
54. Israel A., Martinez-Goss M., Friedlander M. Effect of salinity and pH on growth and agar yield of Gracilaria tenuistipitata var. liui in laboratory and outdoor cultivation [J]. Journal of Applied Phycology,1999,11(6): 543-549.
55. Jeeji Bai N. Competitive exclusion or morphological transformation?A case study with Spirulina fusiformis [J]. Archives of Hydrobiology,1985,71:191-199.
56. Jeeji Bai N., Seshadri C.V. On coiling and uncoiling of trichromes in the genus Spirulina [J]. Archives of Hydrobiology,1980,60:32-47.
57. Keefe P.D., Draper S. An automated machine vision system for the morphometric of new cultivars and plant genebank accessions [J]. Plant Varieties and Seeds,1998,1:1-11.
58. Khanmohammadi M., Garmarudi A.B., Ghasemi K., et al. Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry [J]. Microchemical Journal,2009,91(1): 47-52.
59. Lankmayr E., Mocak J., Serdt K., et al. Chemometricat classification of pumpkin seed oils using UV-Vis, NIR and FTIR spectra [J]. Journal of Biochemical and Biophysical Methods,2004,61(1-2):95-106.
60. Lasko D.R., Wang D.I.C. On-line monitoring of intracellular ATP concentration in Escherichia coli fermentations [J]. Biotechnology and Bioengineering,1996,52(3):364-372.
61. Latecki L.J., Lakamper R. Convexity rule for shape decomposition based on discrete contour evolution [J]. Computer Vision and Image Understanding,1999,73(3):441-454.
62. Latecki L.J., Lakamper R. Shape similarity measure based on correspondence of visual parts[J]. Ieee Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1185-1190.
63. Lee J.H., Choung M.G. Determination of protein content in pea by near infrared spectroscopy [J]. Food Science and Biotechnology,2009,18(1):60-65.
64. Lee S.M., Kim S., Park J.B., et al. Prediction of chemical compositions for on-line quality measurement of red pepper powder using near infrared reflectance Spectroscopy (NIRS) [J]. Food Science and Biotechnology, 2005,14(2):280-285.
65. Lewin R.A. Uncoiled variants of Spirulina platensis (Cyanophyceae:Oscillatoriaceae) [J]. Algological Studies, 1980,26:48-52.
66. Li P.L., Miao X.L., Li R.X., et al. In Situ Biodiesel Production from Fast-Growing and High Oil Content Chlorella pyrenoidosa in Rice Straw Hydrolysate [J]. Journal of Biomedicine and Biotechnology,2011:
67. Liu B.P., Qin H.J., Luo X., et al. Determination of four contents of feedstuff powder using near infrared Spectroscopy by PLS-BP model [J]. Spectroscopy and Spectral Analysis,2007,27(10):2005-2009.
68. Ma Q., Hao G.Q., Qiao Y.J., et al. Determination of the artificial bezoar powder in bezoar powder by near-infrared spectrometry and support vector machine [J]. Spectroscopy and Spectral Analysis,2006,26(10): 1842-1845.
69. Macaloney G., Hall J.W., Rollins M.J., et al. The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant Escherichia coli production process [J]. Bioprocess Engineering,1997,17(3):157-167.
70. Mairet F., Bernard O., Masci P., et al. Modelling neutral lipid production by the microalga Isochrysis aff. galbana under nitrogen limitation [J]. Bioresource Technology,2011,102(1):142-149.
71. Makrides M., Neumann M., Simmer K., et al. Are long-chain polyunsaturated fatty-acids essential nutrients in infancy [J]. Lancet,1995,345(8963):1463-1468.
72. Mannina L., D'Imperio M., Capitani D., et al. H-1 NMR-based protocol for the detection of adulterations of refined olive oil with refined hazelnut oil [J]. Journal of Agricultural and Food Chemistry,2009,57(24): 11550-11556.
73. Maraboli A., Cattaneo T.M.P., Giangiacomo R. Detection of vegetable proteins from soy, pea and wheat isolates in milk powder by near infrared spectroscopy [J]. Journal of near Infrared Spectroscopy,2002,10(1): 63-69.
74. Mauer L.J., Chernyshova A.A., Hiatt A., et al. Melamine detection in infant formula powder using near-and mid-infrared spectroscopy [J]. Journal of Agricultural and Food Chemistry,2009,57(10):3974-3980.
75. Medina A.R., Gimenez A.G., Camacho F.G., et al. Concentration and purification of stearidonic, eicosapentaenoic, and docosahexaenoic acids from cod-liver oil and the marine microalga isochrysis-galbana [J]. Journal of the American Oil Chemists Society,1995,72(5):575-583.
76. Miao X.L., Wu Q.Y. High yield bio-oil production from fast pyrolysis by metabolic controlling of Chlorella protothecoides [J]. Journal of Biotechnology,2004,110(1):85-93.
77. Min M., Lee W.S. Determination of significant wavelengths and prediction of nitrogen content for citrus [J]. Transactions of the Asae,2005,48(2):455-461.
78. Min R.W., Nielsen J., Villadsen J. On-line monitoring of glucose and penicillin by sequential injection analysis [J]. Analytica Chimica Acta,1996,320(2-3):199-205.
79. Mori T.A., Bao D.Q., Burke V., et al. Docosahexaenoic acid but not eicosapentaenoic acid lowers ambulatory blood pressure and heart rate in humans [J]. Hypertension,1999,34(2):253-260.
80. Muhling M., Harris N., Belay A., et al. Reversal of helix orientation in the cyanobacterium Arthrospira[J]. Journal of Phycology,2003,39(2):360-367.
81. Ng C.L., Wehling R.L., Cuppett S.L. Method for determining frying oil degradation by near-infrared spectroscopy[J]. Journal of Agricultural and Food Chemistry,2007,55(3):593-597.
82. O'shea S.K., Holland F., Bilodeau A. Modeling the effects of salinity and pH on the cadmium bioabsorptive properties of the microalgae isochrysis galbana (T-Iso) in coastal waters [J]. Journal of Coastal Research,2010, 26(1):59-66.
83. Ogbonda K.H., Aminigo R.E., Abu G.O. Influence of temperature and pH on biomass production and protein biosynthesis in a putative Spinlina sp. [J]. Bioresource Technology,2007,98(11):2207-2211.
84. Otsu N. A threshold selection method from Gray-level histograms [J]. IEEE Transactions on Systems, Man, and Cybernetics,1979, SMC-9(1):62-66.
85. Pencreach G., Poisson L., Soultani S., et al. Use of lipases for the production of a DHA-rich lipid fraction from the microalgae Isochrysis galbana [J]. Journal of Biotechnology,2007,131(2):S77-S78.
86. Poisson L., Ergan F. Docosahexaenoic acid ethyl esters from Isochrysis galbana[J]. Journal of Biotechnology, 2001,91(1):75-81.
87. Pope J.A., Nelson R.A., Schaffner C.P., et al. Applications of thin-layer chromatography, high-performance liquid-chromatography and mass-spectrometry in the fermentation and isolation of the antibiotic nybomycin [J]. Journal of Industrial Microbiology,1990,6(1):61-69.
88. Poppsnijders C, Schouten J.A., Heine R.J., et al. Dietary supplementation of omega-3 polyunsaturated fatty-acids improves insulin sensitivity in non-insulin-dependent diabetes [J]. Netherlands Journal of Medicine, 1986,29(2):69-69.
89. Rebolloso-Fuentes M.M., Navarro-Perez A., Garcia-Camacho F., et al. Biomass nutrient profiles of the microalga Nannochloropsis [J]. Journal of Agricultural and Food Chemistry,2001,49(6):2966-2972.
90. Ren G.X., Chen F. Simultaneous quantification of ginsenosides in American ginseng (Panax quinquefolium) root powder by visible/near-infrared reflectance spectroscopy [J]. Journal of Agricultural and Food Chemistry, 1999,47(7):2771-2775.
91. Ribeiro E., Stafslien S.J., Casse F., et al. Automated image-based method for laboratory screening of coating libraries for adhesion of algae and bacterial biofilms [J]. Journal of Combinatorial Chemistry,2008,10(4): 586-594.
92. Ross N.E., Pritchard C.J., Rubin D.M., et al. Automated image processing method for the diagnosis and classification of malaria on thin blood smears [J]. Medical & Biological Engineering & Computing,2006, 44(5):427-436.
93. Roychoudhury P., Harvey L.M., McNeil B. At-line monitoring of ammonium, glucose, methyl oleate and biomass in a complex antibiotic fermentation process using attenuated total reflectance-mid-infrared (ATR-MTR) spectroscopy [J]. Analytica Chimica Acta,2006,561(1-2):218-224.
94. Saleh M.I., Meullenet J.F., Siebenmorgen T.J. Development and Validation of Prediction Models for Rice Surface Lipid Content and Color Parameters Using Near-Infrared Spectroscopy:A Basis for Predicting Rice Degree of Milling [J]. Cereal Chemistry,2008,85(6):787-791.
95. Saoudihelis L., Dubacq J.P., Marty Y., et al. Influence of growth-rate on pigment and lipid-composition of the microalga isochrysis aff galbana clone t-iso [J]. Journal of Applied Phycology,1994,6(3):315-322.
96. Sato T. Nondestructive measurements of lipid content and fatty acid composition in rapeseeds (Brassica napus L.) by near infrared spectroscopy [J]. Plant Production Science,2008,11(1):146-150.
97. Satyarthi J.K., Srinivas D., Ratnasamy P. Estimation of free fatty acid content in oils, fats, and biodiesel by H-1 NMR spectroscopy [J]. Energy & Fuels,2009,23:2273-2277.
98. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures [J]. Analytical Chemistry,1964,36(8):1627-1639.
99. Sheridan C. Big oil bucks for algae [J]. Nature Biotechnology,2009,27:783.
100. Shimamatsu H. A pond for edible Spirulina production and its hydraulic studies [J]. Hydrobiology,1987,151: 83-89.
101. Silveira F.L., Silveira L., Villaverde A.B., et al. Use of dispersive raman spectroscopy in the determination of unsaturated fat in commercial edible oil-and fat-containing industrialized foods [J]. Instrumentation Science & Technology,2010,38(1):107-123.
102. Sinija V.R., Mishra H.N. FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules [J]. Lwt-Food Science and Technology,2009,42(5):998-1002.
103. Siscovick D.S., Raghunathan T.E., King I., et al. Dietary-intake and cell-membrane levels of long-chain n-3 polyunsaturated fatty-acids and the risk of primary cardiac-arrest [J]. Jama-Journal of the American Medical Association,1995,274(17):1363-1367.
104. Spolaore P., Joannis-Cassan C, Duran E., et al. Commercial applications of microalgae [J]. Journal of Bioscience and Bioengineering,2006,101(2):87-96.
105. Srinivasan N., Kasthurikrishnan N., Cooks R.G., et al. Online monitoring with feedback-control of bioreactors using a high ethanol tolerance yeast by membrane introduction mass-spectrometry [J]. Analytica Chimica Acta,1995,316(2):269-276.
106. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999,9(3):293-300.
107. Thiel S., Wiltshire R.J. The automated detection of cyanobacteria using digital image-processing techniques [71. Environment International,1995,21(2):233-236.
108. Tzovenis I., DePauw N., Sorgeloos P. Effect of different light regimes on the docosahexaenoic acid (DHA) content of Isochrysis-Aff-galbana (clone T-ISO) [J]. Aquaculture International,1997,5(6):489-507.
109. Umdu E.S., Tuncer M., Seker E. Transesterification of Nannochloropsis oculata microalga's lipid to biodiesel on AL2O3 supported CaO and MgO catalysts [J]. Bioresource Technology,2009,100(11):2828-2831.
110. Vaccari G., Dosi E., Campi A.L., et al. A near-Infrared spectroscopy technique for the control of fermentation processes-an application to lactic-acid fermentation [J]. Biotechnology and Bioengineering,1994,43(10): 913-917.
111. Valenzuela-Espinoza E., Millan-Nunez R., Nunez-Cebrero F. Protein, carbohydrate, lipid and chlorophyll alpha content in Isochrysis aff. galbana (clone T-Iso) cultured with a low cost alternative to the f/2 medium [J]. Aquacultural Engineering,2002,25(4):207-216.
112. van Bergeijk S.A., Salas-Leiton E., Canavate J.P. Low and variable productivity and low efficiency of mass cultures of the haptophyte Isochrysis aff. galbana (T-iso) in outdoor tubular photobioreactors [J]. Aquacultural Engineering,2010,43(1):14-23.
113. van der Heijden G.W.A.M., Vossepoel A.M., Polder G. Measuring onion cultivars with image analysis using inflection points[J]. Euphytica,1996,87(1):19-31.
114. van Eck J.W., van der Heijden G.W.A.M., Polder G. Accurate measurement of size and shape of cucumber fruits with image analysis Journal of Agricultural Engineering Research,1998,70(4):335-343.
115. van Eykelenburg C. The ultrastructure of Spirulina platensis in relation to temperature and light intensity [J]. Antonie Van Leeuwenhoek,1979,45(3):369-390.
116. Vapnik V.N. Statistical learning theory [M]. New York John Wiley and Sons Inc.,1998.
117. Vardy S., Uwins P. Fourier transform infrared microspectroscopy as a tool to differentiate Nitzschia closterium and Nitzschia longissima [J]. Applied Spectroscopy,2002,56(12):1545-1548.
118. Volkova S.E., Ilyasova N.Y., Ustinov A.V., et al. Methods for analyzing the images of blood preparations [J]. Optics and Laser Technology,1995.27(4):255-261.
119. Vonshak A. Spirulina platensis (Arthrospira):Physiology, Cell-biology and Biotechnology [M]. London Taylor & Francis,1997.
120. Wang L., Lee F.S.C., Wang X.R., et al. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR[J]. Food Chemistry,2006,95(3):529-536.
121. Wang Z.P., Chen S.M., Jia X.M., et al. The effect of environmental factors and gamma-rays on the morphology and growth of Spirulina platensis [J]. The Journal of Zhejiang Agricultural University,1997,23: 36-40.
122. Wang Z.P., Zhao Y. Morphological reversion of Spirulina (Arthrospira) platensis (Cyanophyta):From linear to helical [J]. Journal of Phycology,2005,41(3):622-628.
123. Werman M.J., Sukenik A., Mokady S. Effects of the marine unicellular alga Nannochloropsis sp to reduce the plasma and liver cholesterol levels in male rats fed on diets with cholesterol [J]. Bioscience Biotechnology and Biochemistry,2003,67(10):2266-2268.
124. White S.F., Tothill I.E., Newman J.D., et al. Development of a mass-producible glucose biosensor and flow-injection analysis system suitable for on-line monitoring during fermentations [J]. Analytica Chimica Acta,1996,321(2-3):165-172.
125. Williams P.C. Near-Infrared Technology in the Agricultural and Food Industries [M]. Saint Paul, MN, USA American Association of Cereal Chemists,2001.
126. Wu D., Cao F., Feng S.J., et al. Application of infrared spectroscopy technique to protein content fast measurement in milk powder based on support vector machines [J]. Spectroscopy and Spectral Analysis, 2008a,28(5):1071-1075.
127. Wu D., Cao F., Zhang H., et al. Study on disease level classification of rice panicle blast based on visible and near infrared spectroscopy [J]. Spectroscopy and Spectral Analysis,2009a,29(12):3295-3299.
128. Wu D., Chen X.J., Shi P.Y., et al. Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination [J]. Analytica Chimica Acta,2009b,634(2):166-171.
129. Wu D., Feng S., He Y. Infrared spectroscopy technique for the nondestructive measurement of fat content in milk powder [J]. Journal of Dairy Science,2007,90(8):3613-3619.
130. Wu D., Feng S., He Y. Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis [J]. Journal of Dairy Science,2008b,91(3):939-949.
131. Wu D., He Y., Feng S. Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment [J]. Analytica Chimica Acta,2008c,610(2):232-242.
132. Wu D., He Y., Feng S.J., et al. Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM [J]. Journal of Food Engineering,2008d,84(1):124-131.
133. Wu D., He Y., Nie P.C., et al. Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice [J]. Analytica Chimica Acta,2010,659(1-2):229-237..
134. Wu D., He Y., Shi J.H., et al. Exploring Near and Midinfrared Spectroscopy to Predict Trace Iron and Zinc Contents in Powdered Milk [J]. Journal of Agricultural and Food Chemistry,2009c,57(5):1697-1704.
135. Wu D., Jin C.H., He Y. Study on Combinatorial Optimization of Spectral Principal Components Using Successive Projections Algorithm [J]. Spectroscopy and Spectral Analysis.2009d,29(10):2734-2737.
136. Wu D., Nie P.C., Cuello J., et al. Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder [J]. Journal of Food Engineering, 2011,102(3):278-286.
137. Wu D., Wu H.X., Cai J.B., et al. Classifying the Species of Exopalaemon by Using Visible and near Infrared Spectra with Uninformative Variable Elimination and Successive Projections Algorithm [J]. Journal of Infrared and Millimeter Waves,2009e,28(6):423-427.
138. Wu D., Yang H.Q., Chen X.J., et al. Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine [J]. Journal of Food Engineering,2008e,88(4): 474-483.
139. Wu H.Y., Gao K.S., Villafane V.E., et al. Effects of solar UV radiation on morphology and photosynthesis of filamentous cyanobacterium Arthrospira platensis [J]. Applied and Environmental Microbiology,2005,71(9): 5004-5013.
140. Xu L., Ji G.R., Wang N.Q., et al. An approach for image compression of algae cell using multiwavelets [J]. Advanced Intelligent Computing Theories and Applications,2007.2:836-843,1375.
141. Yu C.C.. Chen H.W., Chen M.J., et al. Chemical Composition and Bioactivities of the Marine Alga Isochrysis galbana from Taiwan [J]. Natural Product Communications,2010,5(12):1941-1944.
142. Zou X.B., Zhao J.W., Huang X.Y., et al. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of'Fuji'apple based on different PLS models [J]. Chemometrics and Intelligent Laboratory Systems,2007,87(1):43-51.
143.蔡隽,鲍旭东,吴磊,et al基于活动轮廓模型的彩色白细胞图像自动分割方法研究[J].生物医学工程研究,2005,(04)：218-222.
144.曹红宝.基于双光谱有毒赤潮优势藻图像分析的研究[D].天津大学,2005.
145.陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用[J].分析测试学报,2007,(01)：66-69.
146.单杨,朱向荣,许青松,et al近红外光谱结合小波变换-径向基神经网络用于奶粉蛋白质与脂肪含量的测定[J].红外与毫米波学报,2010,(02)：128-131.
147.邓月娥,周群,孙素琴.FTIR光谱法与奶粉的品质分析[J].光谱学与光谱分析,2005,(12)：1972-1974.
148.杜聪,王世新,周艺, et al利用Hyperion高光谱数据的三波段法反演太湖叶绿素a浓度[J].环境科学,2009,(10)：2904-2910.
149.顾小红,任璐,陈尚卫, et al.核磁共振技术在食品研究中的应用[J].食品工业科技,2005,(09)：189-191+194.
150.韩冰,周虹丽,朱建华, et al.实验室混合藻种的光谱分解方法研究[J].海洋技术,2009,(04)：58-62.
151.韩锦华,王雪青,苗惠.三种海洋微藻抗肿瘤活性的研究[J].天津科技大学学报,2006,(04)：25-28.
152.何汝洪.大螺旋藻的形态观察和培养试验[J].水生生物学报,1987,(04)：377-380.
153.胡锅亮,吕秀阳,吴建国, et al.近红外漫反射光谱法直接测定银杏叶提取物粉末中总黄酮的含量[J]药物分析杂志,2004,(0])：18-20.
154.胡鸿钧.国外螺旋藻生物技术的现状及发展趋势[J].武汉植物学研究,1997,(04)：369-374.
155.胡开辉,汪世华.小球藻的研究开发进展[J].武汉工业学院学报,2005,(03)：27-30.
156.黄振华.湛江等鞭金藻有机碳营养生态位研究[D].暨南大学,2007.
157.冀克俭,刘元俊,张银生,et al.鱼油多烯脂肪酸乙酯的NMR表征[J].化学分析计量,2001,(03)：3-5.
158.姜悦.乳制品中的DHA：微藻油还是鱼油?[J].食品工业科技,2006,(12)：191-192.
159.蒋汉明,高坤山.两种海洋微藻脂肪酸组成研究[J].科学技术与工程,2003,(01)：46-48.
160.金华丽,卞科.近红外光谱法检测小麦粉中的水分含量[J].中国粮油学报,2010a,(08)：109-112.
161.金华丽,王金水.近红外光谱法检测小麦粉中灰分含量的研究[J].河南工业大学学报(自然科学版),2010b,(01)：14·17,21.
162.李家旺,司民真.傅里叶变换红外光谱法研究核辐照对螺旋藻粉成分的影响[J].光谱实验室,2009,(04)：888-892.
163.李晋楠,汪志平. RAPD分子标记技术用于螺旋藻(Spirulina)分类的研究[J].海洋与湖沼,2002,(02)：203－208.
164.李神州,李岳,陶明方.浅谈螺旋藻的开发价值及应用[J].科技资讯,2008,(14)：202+204.
165.李文权,王清池,陈清花,et al.超声波对球等鞭金藻脂肪酸组成的效应研究[J].海洋科学,2000,(04)：7－9.
166.李秀波,徐旭东,孔任秋.五种微绿球藻产油和产多不饱和脂肪酸的研究[J].水生生物学报,2010,(05)：893－897.
167.粱燕茹,李文权,刘四光.自养小球藻硫酸多糖的红外光谱分析[J].海洋科学,2007,(11);1－4.
168.刘汉元全.积极开展微藻研究确保我国长远粮食安全.人民政协报,(2010).p.App.B03.
169.刘惠芳.螺旋藻在饲料工业中的应用[J].动物科学与动物医学,2001,(02)：62-65.
170.刘世龙.红外光谱法对玉米粉和乙醇中水分含量的检测研究[D].长春理工大学,2010.
171.刘世名.小球藻(Chlorella Vulgaris)高密度异养培养[D].华南理工大学,1999.
172.陆新泉,李宁,陈世福.形态、颜色特征及神经网络在肺癌细胞识别中的应用研究[J].计算机辅助设计与图形学学报,2001,(01)：87-92.
173.罗立民,杨晓敏.白细胞图像的计算机自动分割——白细胞的自动分类研究[J].北京生物医学工程,1991,(04)：195-201.
174.马本学,应义斌,饶秀勤, et al高光谱成像在水果内部品质无损检测中的研究进展[J].光谱学与光谱分析,2009,(06)：161]-1615.
175.马增岭.阳光辐射变化对经济蓝藻螺旋藻形态、光合作用及生长的影响[D].汕头大学,2008.
176.尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理算法的作用及其发展[J].药物分析杂志,2008,(05)：824-829.
177.齐金峰,熊华,陈振林,et al.海藻油微胶囊中DHA检测预处理算法的比较[J].食品研究与开发,2009,(07)：13·17.
178.齐沛沛,王飞.制备生物柴油用小球藻的油脂富集培养研究[J].现代化工,2008,(S2)：36-39+41.
179.秦松,吴沿友,陈娟,et al显微彩色图像处理在藻类识别统计中的应用[J].农机化研究,2006,(06)：199-201+203.
180.邱江,叶勤,张嗣良.红外光谱技术在生物过程监测中的应用[J].生物工程学报,1998,(01).
181.松文.海藻生产生物柴油的潜力和前景[J].精细化工原料及中间体,2008,(12)：21.23,20.
182.苏敏,王丹红,郑能雄.近红外漫反射光谱法快速测定肉骨粉的成分[J].医学动物防制,2004,(10)：602-604.
183.孙勇.基于双光谱有毒赤潮藻图像自动识别系统的研究[D].天津大学,2007.
184.唐启义,冯明光.实用统计分析及其DPS数据处理系统.北京科学出版社,2002.
185.唐赢中,黄利群,严国安.藻类产烃研究进展[J].生物工程进展,1997,(02).
186.唐玉莲,梁逸曾,范伟,et al.应用近红外光谱快速鉴别不同年龄段人食用的奶粉品种[J].红外,2010,(01)：30-35.
187.田国辉,陈亚杰,冯清茂.拉曼光谱的发展及应用[J].化学工程师,2008,(01)：34-36.
188.汪志平,陈声明,贾小明,et al.环境因子和Y—射线对钝顶螺旋藻形态及生长的影响[J].浙江农业大学学报,1997,(01).
189.汪志平,贾小明,傅俊杰, et al不同形态钝顶螺旋藻藻丝体分离和纯化的方法[J].浙江农业学报,1998,(05).
190.汪志平.蛋白质SDS-PAGE用于螺旋藻分类及突变体鉴定的研究[J].浙江大学学报(农业与生命科学版),2000,(06).
191.王翠红,李日强,辛晓芸.钝项螺旋藻处理氨氮废水的研究[J].上海环境科学,2002,(12)：728－731+754.
192.王军锋,马金才,徐斌,et al.酿酒废水培养钝顶螺旋藻(S.platensis)与营养物质去除的研究[J].城市环境与城市生态,2002,(05)：42-44.
193.王雷,乔晓艳,董有尔,et al高光谱图像技术在农产品检测中的应用进展[J].应用光学,2009,(04)：639-645.
194.王以斌.梁强,何碧娟, et al.螺旋藻多糖酶解辅助超声波提取工艺的优化[J].现代农业科技,2009,(08)：234-235.
195.韦成礼,李永健,计玲华.国内外螺旋藻的研究和开发利用现状广西农业大学学报,1994,(04).
196.翁欣欣,陆峰,王传现,et al.近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析[J].光谱学与光谱分析.2009,(12)：3283-3287.
197.吴静珠,王一鸣,张小超,et al.基于近红外的奶粉品质检测技术研究[J].光谱学与光谱分析,2007a,(09)：1735-1738.
198.吴静珠,王一鸣,张小超,et al.支持向量机-近红外光谱法用于真假奶粉的判别[J].农机化研究,2007b,(01)：155－158.
199.吴庆余,殷实,盛国英,et al.异养黄化藻类产烃研究新发现[J].中国科学(B辑化学生命科学地学),1993,(04)：423－429.
200.吴文龙,杨志娟.螺旋藻保健食品的功能因子与研究开发进展[J].食品研究与开发,2006,(02)：129-131.
201.徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,(02)：134-142.
202.杨莉,魏少华,吴建国.用近红外反射光谱法测定乳清粉的品质性状叨.乳业科学与技术,2001,(03)：22-25.
203.杨晓敏,罗立民,韦钰. AUTOMATIC CLASSIFICATION METHOD AND SYSTEM FOR LEUKOCYTE IN HUMAN PERIPHERAL BLOOD [J]. Journal of Southeast University(English Edition),1993d, (02):1-7.
204.杨晓敏,罗立民.白细胞自动分类中的特征提取和分析[J].北京生物医学工程,1992.(04)：214-220.
205.杨晓敏,罗立民.白细胞自动分类中的纹理分析方法研究[J].中国生物医学工程学报,1993b,(02)：124－129.
206.杨晓敏,罗立民.人体末梢血液中自细胞自动识别与分类的研究(英文)[J].航天医学与医学工程,1993a,(03)：190-196.
207.杨晓敏,罗立民.血液白细胞的计算机自动分类研究明.东南大学学报,1993c,(06)：106－109.
208.杨晓敏,罗立民,韦钰.人体白细胞自动分类方法与系统实现[J].计算机学报,1994a,(02).
209.杨晓敏,罗立民,韦钰.血液白细胞计算机分类中的特征提取研究应用科学学报,1994b,(02).
210.杨晓敏,罗立民,书钰.皿液白细胞图象的计算机自动分割方法研究[J].中国生物医学工程学报,1995，(01).
211.姚守拙,何风姣,聂利华.压电监测大肠杆菌生长过程中培养液参数[J].湖南大学学报(自然科学版),1995,f01).
212.于修烛,杜双奎,岳田利,et al.食用油反式脂肪酸傅里叶红外光谱重组检测技术[J].农业机械学报,2009,(01)：114-119.
213.余颖.微绿球藻的微波辐射效应及其作用机制[D].福建师范大学,2006.
214.岳跃民,王克林,张兵, et al.高光谱遥感在生态系统研究中的应用进展[J].遥感技术与应用,2008,(04)：471-478.
215.张磊.赤潮优势藻图像分析的研究[D].天津大学,2003.
216.张忠义.螺旋藻的生物学评价食品科技,1999,(05)：52-54.
217.赵静,庞其昌,马骥,et al.中药黄连黄柏混合粉末的光谱成像分析技术[J].光学学报,2010,(11)：3259-3263.
218.赵巧华,秦伯强.藻类的光谱吸收特征及其混合藻吸收系数的分离[J].环境科学学报,2008,(02)：313-318.
219.郑立立,王磊,江克铮.鱼油中二十碳五烯酸和二十二碳六烯酸的气相色谱-红外光谱分析[J].分析化学,1994,(04)：383-385.
220.周国泰.豆奶粉红外光谱法质量评价[J].青海大学学报(自然科学版),2008,(06)：40-43.
221.周希明.螺旋藻的开发利用和培养[J].中国水产,1988,6：31-33.
222.周永国,侯文龙,杨越冬,et al傅里叶变换红外光谱法同时测定奶粉中蛋白质和脂肪含量[J].理化检验(化学分册),2009,(01)：66-69,72.