基于近红外高光谱图像技术的栗果品质无损检测
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摘要
提出一种基于近红外高光谱图像技术的板栗果实品质快速无损检测方法。分别选取3个不同品种栗果、1个品种的霉变栗果和1个品种的虫害栗果各30个样品,采集供试样品的近红外高光谱数据;采用偏最小二乘法(PLS)建立栗果中总糖和淀粉含量预测模型,预测值与实际值的相关系数为0.9313～0.9587,均方根误差为0.062 4～0.225 0;结合主成分分析法(PCA),建立不同品种栗果鉴别以及识别霉变、虫害、正常栗果的判别分析(DA)模型,模型的识别率分别为96.7%和98.6%。结果表明,近红外高光谱图像技术可用于栗果总糖和淀粉的定量预测,以及不同品种栗果和霉变、虫害果的快速定性识别。
In this paper, a rapid and non-destructive detection method was proposed for quality inspection of Castanea mollissima nuts using near-infrared hyperspectral imaging techniques. Thirty samples of three different types, i.e. normal nuts, mouldy nuts and insect infested nuts were selected, and their near-infrared hyperspectral data were collected. The quantitative analysis model for the contents of sugar and starch was established using partial least squares(PLS) method. The coefficients between the predictive values and the actual values were between 0.931 3 and 0.958 7, and the root mean squared errors were between 0.062 4 and 0.225 0. Combining principal component analysis(PCA), the discriminant analysis(DA) model for the detection of different nut varieties and for the identification of the mouldy, the insect infested and the normal nuts was established, and their recognition accuracies of the established model were 96.7% and 98.6%, respectively. The results showed that near-infrared hyperspectral imaging techniques can not only be used for quantitative analysis of total sugar and starch in Castanea mollissima nuts, but also be used for the rapid detection of different nut varieties and the mouldy or the insect infested nuts.
In this paper, a rapid and non-destructive detection method was proposed for quality inspection of Castanea mollissima nuts using near-infrared hyperspectral imaging techniques. Thirty samples of three different types, i.e. normal nuts, mouldy nuts and insect infested nuts were selected, and their near-infrared hyperspectral data were collected. The quantitative analysis model for the contents of sugar and starch was established using partial least squares(PLS) method. The coefficients between the predictive values and the actual values were between 0.931 3 and 0.958 7, and the root mean squared errors were between 0.062 4 and 0.225 0. Combining principal component analysis(PCA), the discriminant analysis(DA) model for the detection of different nut varieties and for the identification of the mouldy, the insect infested and the normal nuts was established, and their recognition accuracies of the established model were 96.7% and 98.6%, respectively. The results showed that near-infrared hyperspectral imaging techniques can not only be used for quantitative analysis of total sugar and starch in Castanea mollissima nuts, but also be used for the rapid detection of different nut varieties and the mouldy or the insect infested nuts.
引文
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[16]CEN H Y,LU R F,ZHU Q B,et al.Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification[J].Postharvest Biol Tec,2016,111:352-361.
[17]张初,刘飞,孔汶汶,等.利用近红外高光谱图像技术快速鉴别西瓜种子品种[J].农业工程学报,2013,29(20):270-277.
[18]VETREKAR N T,GAD R S,FERNANDES I,et al.Non-invasive hyperspectral imaging approach for fruit quality control application and classification:case study of apple,chikoo,guava fruits[J].J Food Sci Technol,2015,52(11):6978-6989.
[19]刘洁,李小昱,李培武,等.基于近红外光谱的板栗水分检测方法[J].农业工程学报,2010,26(2):338-341.
[20]展慧,李小昱,周竹,等.基于近红外光谱和机器视觉融合技术的板栗缺陷检测[J].农业工程学报,2011,27(2):345-349.
[21]周竹,李小昱,李培武,等.基于GA-LSSVM和近红外傅里叶变换的霉变板栗识别[J].农业工程学报,2011,27(3):331-335.
[22]郑剑,周竹,仲山民,等.基于近红外光谱与随机青蛙算法的褐变板栗识别[J].浙江农林大学学报,2016,33(2):322-329.
[2]刘国彬,兰彦平,兰卫宗,等.板栗农家品种资源坚果表型性状分析[J].江西农业大学学报,2013,35(5):977-981.
[3]史艳飞,张咏琦.山阳县板栗生产存在的问题及发展对策[J].现代农村科技,2017(10):92.
[4]李江波,饶秀勤,应义斌.农产品外部品质无损检测中高光谱成像技术的应用研究进展[J].光谱学与光谱分析,2011,31(8):2021-2026.
[5]张保华,李江波,樊书祥,等.高光谱成像技术在果蔬品质与安全无损检测中的原理及应用[J].光谱学与光谱分析,2014,34(10):2743-2751.
[6]YANG Y C,SUN D W,PU H B,et al.Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion[J].Postharvest Biol and Tec,2015,103:55-65.
[7]吕刚.基于光谱和多光谱成像技术的葡萄内部品质快速无损检测和仪器研究[D].杭州:浙江工业大学,2013.
[8]李瑞,傅隆生.基于高光谱图像的蓝莓糖度和硬度无损测量[J].农业工程学报,2017,33(z1):362-366.
[9]QIN J W,BURKS T F,KIM M S,et al.Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method[J].Sens Instrum Food Qual Saf,2008,2(3):168-177.
[10]李江波,饶秀勤,应义斌,等.基于高光谱成像技术检测脐橙溃疡[J].农业工程学报,2010,26(8):222-228.
[11]田有文,牟鑫,程怡.高光谱成像技术无损检测水果缺陷的研究进展[J].农机化研究,2014,36(6):1-5.
[12]YU K Q,ZHAO Y R,LIU Z Y,et al.Application of visible and near-infrared hyperspectral imaging for detection of defective features in loquat[J].Food Bioprocess Tech,2014,7(11):3077-3087.
[13]LI J B,CHEN L P,HUANG W Q,et al.Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging[J].Food Bioprocess Tech,2016,112:121-133.
[14]吴龙国.基于高光谱成像技术的灵武长枣常见缺陷无损检测研究[D].银川:宁夏大学,2014.
[15]史崇升.基于高光谱成像技术的马铃薯外部品质无损检测建模及优化研究[D].银川:宁夏大学,2014.
[16]CEN H Y,LU R F,ZHU Q B,et al.Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification[J].Postharvest Biol Tec,2016,111:352-361.
[17]张初,刘飞,孔汶汶,等.利用近红外高光谱图像技术快速鉴别西瓜种子品种[J].农业工程学报,2013,29(20):270-277.
[18]VETREKAR N T,GAD R S,FERNANDES I,et al.Non-invasive hyperspectral imaging approach for fruit quality control application and classification:case study of apple,chikoo,guava fruits[J].J Food Sci Technol,2015,52(11):6978-6989.
[19]刘洁,李小昱,李培武,等.基于近红外光谱的板栗水分检测方法[J].农业工程学报,2010,26(2):338-341.
[20]展慧,李小昱,周竹,等.基于近红外光谱和机器视觉融合技术的板栗缺陷检测[J].农业工程学报,2011,27(2):345-349.
[21]周竹,李小昱,李培武,等.基于GA-LSSVM和近红外傅里叶变换的霉变板栗识别[J].农业工程学报,2011,27(3):331-335.
[22]郑剑,周竹,仲山民,等.基于近红外光谱与随机青蛙算法的褐变板栗识别[J].浙江农林大学学报,2016,33(2):322-329.
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