近红外光谱技术检测灵武长枣果肉硬度和贮藏时间
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摘要
利用近红外光谱(400～1 000 nm)系统采集140个灵武长枣样本的光谱信息,采用不同方法预处理原始光谱数据,优选出最佳预处理方法。分别建立竞争性自适应加权算法(CARS)和连续投影算法(SPA)提取特征变量的果肉硬度偏最小二乘回归(PLSR)预测模型,并利用原始光谱建立灵武长枣贮藏时间的偏最小二乘判别(PLS-DA)模型。结果表明,去趋势法(Detrend)为最优预处理方法;建立的Detrend-CARS-PLSR模型效果较好,果肉平均硬度校正集和预测集模型相关系数均为0. 868;果肉最大硬度校正集和预测集模型相关系数分别为0. 914、0. 849。建立的贮藏时间PLS-DA判别模型的校正集判别准确率为98%,预测集判别准确率为99%。说明,采用近红外光谱技术对灵武长枣贮藏过程中长枣果肉硬度和贮藏时间的快速预测具有可行性。
Spectral information of 140 Lingwu long jujube samples was collected by using near-infrared spectroscopy( 400-1 000 nm). Different methods were applied to preprocess the original spectrum,and the optimal pretreatment method was selected. The competitive adaptive reweighed sampling( CARS) and successive projections algorithm( SPA) were used to select characteristic wavelengths,and the partial least squares regression( PLSR) model was established based on characteristic wavelengths for predicting flesh firmness of Lingwu long jujube. The partial least squares discriminate analysis( PLSDA) models of long jujube storage time were established based on full spectrum. The results indicated that the Detrend method was the optimal pretreatment method,the Detrend-CARS-PLSR model was the best,and correlation coefficients of average flesh firmness for calibration set and prediction set were 0.868 and 0.868,and correlation coefficients of maximum flesh firmness for calibration set and prediction set were 0.914 and 0.849,respectively. The PLS-DA discriminant model of storage time was established and the discrimination accuracy of calibration set and prediction set were 98% and 99%. In conclusion,it is feasible to predict flesh firmness and storage time of Lingwu long jujube based on near-infrared spectroscopy technique.
Spectral information of 140 Lingwu long jujube samples was collected by using near-infrared spectroscopy( 400-1 000 nm). Different methods were applied to preprocess the original spectrum,and the optimal pretreatment method was selected. The competitive adaptive reweighed sampling( CARS) and successive projections algorithm( SPA) were used to select characteristic wavelengths,and the partial least squares regression( PLSR) model was established based on characteristic wavelengths for predicting flesh firmness of Lingwu long jujube. The partial least squares discriminate analysis( PLSDA) models of long jujube storage time were established based on full spectrum. The results indicated that the Detrend method was the optimal pretreatment method,the Detrend-CARS-PLSR model was the best,and correlation coefficients of average flesh firmness for calibration set and prediction set were 0.868 and 0.868,and correlation coefficients of maximum flesh firmness for calibration set and prediction set were 0.914 and 0.849,respectively. The PLS-DA discriminant model of storage time was established and the discrimination accuracy of calibration set and prediction set were 98% and 99%. In conclusion,it is feasible to predict flesh firmness and storage time of Lingwu long jujube based on near-infrared spectroscopy technique.
引文
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[2]姚佳,胡小松,廖小军,等.高静压对果蔬制品质构影响的研究进展[J].农业机械学报,2013,44(9):118-124,117.
[3]马庆华,王贵禧,梁丽松,等.冬枣的穿刺质地及其影响因素[J].林业科学研究,2011,24(5):596-601.
[4]梁静,孙锐,孙蕾,等.不同品种果桑穿刺试验质构特性分析[J].山东林业科技,2017,47(5):26-30.
[5]杜雪燕,王迅,柴沙驼,等.基于近红外光谱的天然牧草CNCPS组分分析与预测[J].江苏农业学报,2015,31(5):1115-1123.
[6] HUANG J,PENG S. Comparison and standardization among Chlo-rophyll meters in their readings on rice leaves[J].Plant ProductionScience,2004,7(1):97-100.
[7]石鲁珍,陈杰,张树艳,等.基于蒙特卡洛法红枣光谱水分模型研究[J].江苏农业科学,2018,46(14):205-208.
[8]陈辰,鲁晓翔,张鹏,等.基于可见-近红外漫反射光谱技术的葡萄贮藏期间可溶性固形物定量预测[J].食品科学,2015,36(20):109-114.
[9] CARAMES E T S,ALAMAR P D,POPPI R J,et al. Quality con-trol of cashew apple and guava nectar by near infrared spectroscopy[J].Journal of Food Composition&Analysis,2017,56:41-46.
[10] PAZ P,SANCHEZ M T,PEREZMARIN D,et al. EvaluatingNIR instruments for quantitative and qualitative assessment of in-tact apple quality[J]. Journal of the Science of Food&Agricul-ture,2009,89(5):781-790.
[11]闫润,王新忠,邱白晶,等.基于特征光谱的草莓品种快速鉴别[J].农业机械学报,2013,44(9):182-186.
[12]刘燕德,吴明明,孙旭东,等.黄桃表面缺陷和可溶性固形物光谱同时在线检测[J].农业工程学报,2016,32(6):289-295.
[13] NICOLAI B M,THERON K I,LAMMERTYN J. Kernel PLS re-gression on wavelet transformed NIR spectra for prediction of sugarcontent of apple[J]. Chemometrics&Intelligent Laboratory Sys-tems,2007,85(2):243-252.
[14] MA T,LI X,INAGAKI T,et al. Noncontact evaluation of solublesolids content in apples by Near-infrared hyperspectral imaging[J].Journal of Food Engineering,2017,224:53-61.
[15] ELMASRY G,WANG N,ELSAYED A,et al. Hyperspectral ima-ging for nondestructive determination of some quality attributes forstrawberry[J].Journal of Food Engineering,2007,81(1):98-107.
[16]马庆华,王贵禧,梁丽松.质构仪穿刺试验检测冬枣质地品质方法的建立[J].中国农业科学,2011,44(6):1210-1217.
[17]陈亚斌.基于高光谱和荧光高光谱技术的灵武长枣内部成分无损检测研究[D].银川:宁夏大学,2017.
[18] SU W H,BAKALIS S,SUN D W. Fourier transform mid-infrared-attenuated total reflectance(FTMIR-ATR)microspectroscopy fordetermining textural property of microwave baked tuber[J]. Jour-nal of Food Engineering,2018,218:1-13.
[19]张初.基于光谱与光谱成像技术的油菜病害检测机理与方法研究[D].杭州:浙江大学,2016.
[20]左婷.基于高光谱图像技术的夏橙质构特性检测方法研究[D].武汉:华中农业大学,2015.
[21]欧阳爱国,谢小强,刘燕德,等.苹果可溶性固形物近红外在线光谱变量优选[J].农业机械学报,2014,45(4):220-225.
[22] WANG Q,XUE W Q,MA H X,et al. Quantitative analysis ofseed purity for maize usingnear infrared spectroscopy[J]. Transac-tions of the Chinese Society of Agricultural Engineering,2012:259-264.
[23]黄敏,朱晓,朱启兵,等.基于高光谱图像的玉米种子特征提取与识别[J].光子学报,2012,41(7):868-873.
[24]彭彦昆,赵芳,李龙,等.利用近红外光谱与PCA-SVM识别热损伤番茄种子[J].农业工程学报,2018,34(5):159-165.
[25]黄志明,林素英,傅明连,等.枇杷果实发育过程中果肉质地与胞壁酶活性的变化[J].热带作物学报,2012,33(1):24-29.
[26]商亮,谷静思,郭文川.基于介电特性及ANN的油桃糖度无损检测方法[J].农业工程学报,2013,29(17):257-264.