冷季豆品质性状近红外模型建立及区域分析
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摘要
豌豆（Pisum sativum L.）和蚕豆（Vicia faba L.）是我国重要的冷季食用豆类作物，营养丰富，分布广泛，品种多样，是重要的种质资源。一直以来，国际市场都将食用豆作为高价值高价格产品进行贸易，随着我国生活水平的提高，人们对食用豆健康营养的消费需求越来越强。面对外部市场需求的不断扩大，做好豌豆和蚕豆的种质品质性状研究，既有利于种质选种育种和产地种植，也为加工储运、工艺和设备选择提供依据。
本研究以品质性状为主线，通过化学方法检测了256份豌豆和244份蚕豆中蛋白质、淀粉、脂肪和总多酚的含量，探讨了品质性状间的相关性，并筛选出了品质性状相对含量较高的豌豆96份次，蚕豆种质102份次，分别占37.5%和41.8%。
采用目测法，研究了豌豆种质表观性状粒形、粒色和脐色的单项和组合，蚕豆种质的粒长、粒色和脐色单项和组合与品质性状的关系，其中品质性状随单项或多项表观性状均有显著差异特点（P<0.05）。表观性状组合预估品质的准确度范围为57.1%~100%，变异系数较小。
本文研究了豌豆和蚕豆种质品质性状的快速筛选方法。在25℃条件下，通过探讨仪器分辨率、扫描次数以及样品粒径对粉末样品平均光谱和SD光谱的影响，明确了本实验中适宜建模条件为分辨率16cm-1，扫描次数64次，样品粉末粒度60目。以豌豆籽粒模型为例，对影响模型的稳健性的样品温度和籽粒水分含量因素进行了探讨，发现扫描温度和含水量越接近建模样品条件，模型稳定性越好。
以化学检测数据为参考值，采用偏最小二乘法（PLS），对190份豌豆和244份蚕豆的粉末和籽粒建立了近红外回归模型，粉末模型整体优于籽粒模型，其中豌豆粉末蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为0.99、0.99、0.94和0.95；籽粒蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为0.97、0.95、0.94和0.94；蚕豆粉末蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为0.97、0.93、0.81和0.89，蚕豆籽粒蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为088、0.89、0.81和0.84。相关系数较高说明豌豆和蚕豆近红外模型具有较高预测准确度。
应用两步聚类分析，研究了冷季豆的主要品质性状和产地区域的关系，分别得到豌豆种质的品质-产地聚类模式：聚类1低蛋白质含量-北部中部区，聚类2低淀粉含量-西部区，聚类3高蛋白质、淀粉和脂肪含量-西南部区；蚕豆种质的品质-产地聚类模式：聚类1高脂肪含量-西南部区，聚类2高蛋白质含量-东、北部中部区，聚类3高淀粉和脂肪含量-西部区。产地聚类模式与播种期、经度、纬度和海拔高度对冷季豆品质性状的实际影响相一致。
Field pe（aPisum sativum L.）and faba bean (Vicia faba L.) are important cold-season grain legumescultivated in China. Cold-season grain legumes are popularly nutritional sources of rich protein,carbohydrates and fiber, as well as many total polyphenol, essential vitamins and minerals but low oiland sodium. Both legumes are grown at altitude from hundreds meters to three or four thousands metersfar from city pollution and low fertilizer needed, in which ecological environment are clean and healthy.In global trade, field pea and faba bean are always considered as high-value products due to nutrientvalue and excellent ecological environment, as well as expensive labor cost. Consumers have becomemore health conscious, demanding and willing to pay for the “good quality”. Quality traits of the foodlegumes are essential identify index for the choice of germplasm resource, food procedure and evenprocessing equipments designed.
This research analyzed the content of protein, starch, oil and total polyphenol in field pea (256varieties) and faba bean (244varieties) by chemical methods. The coefficient of correlation (r2) betweenquality traits was calculated. The legumes germplasm with excellent quality traits were screened and atotal of96pea accessions and102faba bean accessions were obtained, with the ratio of37.5%and41.8%, respectively.
By visual-observe method, the relation between apparent traits (seed shape, seed coat color andhilum color) and quality traits of pea accessions were researched. The difference of quality traits of peaaccessions in certain sorts of seed shapes, seed coat color and hilum color was significant (P<0.05).Also faba bean did in certain sorts of seed length, seed coat colors and hilum colors be significant (P<0.05). Range of accuracy of prediction by combined apparent traits was from57.1%to100%withlower value of coefficient of variation.
Feasibility of the Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) on estimating qualitytraits in pea and faba bean were evaluated in current study, respectively. Firstly, using pea powder assample, the influence of scanning conditions, such as different resolution, different scan times andsample granularity, were identified to unify the scanning term to get high quality spectra. Spectra wereobtained by Matrix-I FT-NIR spectrometer (Bruker Optics, Ettlingen, German) at25℃with repeated9times at different levels of resolutions, scan times and particle size and performed by OPUS6.5foraverage spectrum. By the difference of value of absorption bands and SD patterns of the average spectra,optimal scanning conditions were resolution of16cm-1, scanning of64times, and sample particle sizeof60mush.
Secondly, estimation models were developed for protein, starch, oil, and total polyphenol of peaand faba bean using near infrared spectroscopy (NIRS), respectively. A total of190pea samples weremeasured in both milled powder and intact seed forms. Partial least squares (PLS) regression wasapplied for model development. The optimal models were powder-based for protein and starch withresidual predictive deviation (RPD) of5.88and5.82as well as coefficients of correlation (r2) of0.99 and0.99, respectively. The optimal models were seed-based for protein, starch, oil and total polyphenolwith coefficients of correlation (r2) of0.97,0.95,0.94, and0.94, respectively. High values of correlationcoefficient (r2) revealed that models had good predictive capacities for rapid germplasm analysis of pea.A total of244faba bean samples were also measured in both milled powder and intact seed forms.Models of powder were generally superior to models in intact seed. The optimal seed powder-basedmodels for protein, starch, oil and total polyphenol had coefficients of correlation (r2) of0.97,0.93,0.81,and0.89, respectively. The optimal models of faba bean were seed-based for protein, starch, oil andtotal polyphenol with coefficients of correlation (r2) of0.88,0.89,0.81, and0.84, respectively. Thirdly,the influence of temperature and water content of samples for prediction of pea seed models of proteinand starch were measured. The results showed robust prediction were obtained when temperature andwater content of samples were close to the condition of samples in calibration set. Temperature regionwas above15℃and best at25℃. The content of water was below11.2%near to calibration samplesof ambient temperature drying.
To explore the relationship between quality traits and producing regions,150pea varieties withspecific information were analyzed by two-step cluster analysis. Three distinct groupings were obtainedwith obvious features. Group1was in low protein content at production area of North and Central China.Group2was in low starch content at production area of West China. Group3was in high protein, starchand oil content at production area of Southwest China. The clustering accuracy was62.5%. Therelationship between nutrient contents and producing areas of faba bean accessions were determined bytwo-step cluster analysis. Three distinct groupings of faba bean were obtained with region-constituentfeatures, i.e., Group1of high oil at production area of Southwest China, Group2of high protein atproduction area of North and Central China, and Group3of high starch as well as total polyphenol atproduction area of West China. The clustering accuracy was79.5%. The value of clustering accuracymight be related with influence of cultivating location on survive of the crops. Moreover, the nutritioncontents were affected by seeding date, longitude, latitude, and altitude of plant location. Clusteranalysis revealed that the contents of quality traits in both legumes were strongly influenced bygeographical factors.
本研究以品质性状为主线，通过化学方法检测了256份豌豆和244份蚕豆中蛋白质、淀粉、脂肪和总多酚的含量，探讨了品质性状间的相关性，并筛选出了品质性状相对含量较高的豌豆96份次，蚕豆种质102份次，分别占37.5%和41.8%。
采用目测法，研究了豌豆种质表观性状粒形、粒色和脐色的单项和组合，蚕豆种质的粒长、粒色和脐色单项和组合与品质性状的关系，其中品质性状随单项或多项表观性状均有显著差异特点（P<0.05）。表观性状组合预估品质的准确度范围为57.1%~100%，变异系数较小。
本文研究了豌豆和蚕豆种质品质性状的快速筛选方法。在25℃条件下，通过探讨仪器分辨率、扫描次数以及样品粒径对粉末样品平均光谱和SD光谱的影响，明确了本实验中适宜建模条件为分辨率16cm-1，扫描次数64次，样品粉末粒度60目。以豌豆籽粒模型为例，对影响模型的稳健性的样品温度和籽粒水分含量因素进行了探讨，发现扫描温度和含水量越接近建模样品条件，模型稳定性越好。
以化学检测数据为参考值，采用偏最小二乘法（PLS），对190份豌豆和244份蚕豆的粉末和籽粒建立了近红外回归模型，粉末模型整体优于籽粒模型，其中豌豆粉末蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为0.99、0.99、0.94和0.95；籽粒蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为0.97、0.95、0.94和0.94；蚕豆粉末蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为0.97、0.93、0.81和0.89，蚕豆籽粒蛋白质、淀粉、脂肪和总多酚模型的相关系数分别为088、0.89、0.81和0.84。相关系数较高说明豌豆和蚕豆近红外模型具有较高预测准确度。
应用两步聚类分析，研究了冷季豆的主要品质性状和产地区域的关系，分别得到豌豆种质的品质-产地聚类模式：聚类1低蛋白质含量-北部中部区，聚类2低淀粉含量-西部区，聚类3高蛋白质、淀粉和脂肪含量-西南部区；蚕豆种质的品质-产地聚类模式：聚类1高脂肪含量-西南部区，聚类2高蛋白质含量-东、北部中部区，聚类3高淀粉和脂肪含量-西部区。产地聚类模式与播种期、经度、纬度和海拔高度对冷季豆品质性状的实际影响相一致。
Field pe（aPisum sativum L.）and faba bean (Vicia faba L.) are important cold-season grain legumescultivated in China. Cold-season grain legumes are popularly nutritional sources of rich protein,carbohydrates and fiber, as well as many total polyphenol, essential vitamins and minerals but low oiland sodium. Both legumes are grown at altitude from hundreds meters to three or four thousands metersfar from city pollution and low fertilizer needed, in which ecological environment are clean and healthy.In global trade, field pea and faba bean are always considered as high-value products due to nutrientvalue and excellent ecological environment, as well as expensive labor cost. Consumers have becomemore health conscious, demanding and willing to pay for the “good quality”. Quality traits of the foodlegumes are essential identify index for the choice of germplasm resource, food procedure and evenprocessing equipments designed.
This research analyzed the content of protein, starch, oil and total polyphenol in field pea (256varieties) and faba bean (244varieties) by chemical methods. The coefficient of correlation (r2) betweenquality traits was calculated. The legumes germplasm with excellent quality traits were screened and atotal of96pea accessions and102faba bean accessions were obtained, with the ratio of37.5%and41.8%, respectively.
By visual-observe method, the relation between apparent traits (seed shape, seed coat color andhilum color) and quality traits of pea accessions were researched. The difference of quality traits of peaaccessions in certain sorts of seed shapes, seed coat color and hilum color was significant (P<0.05).Also faba bean did in certain sorts of seed length, seed coat colors and hilum colors be significant (P<0.05). Range of accuracy of prediction by combined apparent traits was from57.1%to100%withlower value of coefficient of variation.
Feasibility of the Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) on estimating qualitytraits in pea and faba bean were evaluated in current study, respectively. Firstly, using pea powder assample, the influence of scanning conditions, such as different resolution, different scan times andsample granularity, were identified to unify the scanning term to get high quality spectra. Spectra wereobtained by Matrix-I FT-NIR spectrometer (Bruker Optics, Ettlingen, German) at25℃with repeated9times at different levels of resolutions, scan times and particle size and performed by OPUS6.5foraverage spectrum. By the difference of value of absorption bands and SD patterns of the average spectra,optimal scanning conditions were resolution of16cm-1, scanning of64times, and sample particle sizeof60mush.
Secondly, estimation models were developed for protein, starch, oil, and total polyphenol of peaand faba bean using near infrared spectroscopy (NIRS), respectively. A total of190pea samples weremeasured in both milled powder and intact seed forms. Partial least squares (PLS) regression wasapplied for model development. The optimal models were powder-based for protein and starch withresidual predictive deviation (RPD) of5.88and5.82as well as coefficients of correlation (r2) of0.99 and0.99, respectively. The optimal models were seed-based for protein, starch, oil and total polyphenolwith coefficients of correlation (r2) of0.97,0.95,0.94, and0.94, respectively. High values of correlationcoefficient (r2) revealed that models had good predictive capacities for rapid germplasm analysis of pea.A total of244faba bean samples were also measured in both milled powder and intact seed forms.Models of powder were generally superior to models in intact seed. The optimal seed powder-basedmodels for protein, starch, oil and total polyphenol had coefficients of correlation (r2) of0.97,0.93,0.81,and0.89, respectively. The optimal models of faba bean were seed-based for protein, starch, oil andtotal polyphenol with coefficients of correlation (r2) of0.88,0.89,0.81, and0.84, respectively. Thirdly,the influence of temperature and water content of samples for prediction of pea seed models of proteinand starch were measured. The results showed robust prediction were obtained when temperature andwater content of samples were close to the condition of samples in calibration set. Temperature regionwas above15℃and best at25℃. The content of water was below11.2%near to calibration samplesof ambient temperature drying.
To explore the relationship between quality traits and producing regions,150pea varieties withspecific information were analyzed by two-step cluster analysis. Three distinct groupings were obtainedwith obvious features. Group1was in low protein content at production area of North and Central China.Group2was in low starch content at production area of West China. Group3was in high protein, starchand oil content at production area of Southwest China. The clustering accuracy was62.5%. Therelationship between nutrient contents and producing areas of faba bean accessions were determined bytwo-step cluster analysis. Three distinct groupings of faba bean were obtained with region-constituentfeatures, i.e., Group1of high oil at production area of Southwest China, Group2of high protein atproduction area of North and Central China, and Group3of high starch as well as total polyphenol atproduction area of West China. The clustering accuracy was79.5%. The value of clustering accuracymight be related with influence of cultivating location on survive of the crops. Moreover, the nutritioncontents were affected by seeding date, longitude, latitude, and altitude of plant location. Clusteranalysis revealed that the contents of quality traits in both legumes were strongly influenced bygeographical factors.
引文
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