Online detection of soluble solids contents for “Yuluxiang” pear by visible-near infrared diffuse transmission spectroscopy

Authors

LIU Yande, East China Jiaotong University, Nanchang, Jiangxi 330013, China;Institute of Optical and Electrical Machinery Technology and Application, Nanchang, Jiangxi 330013, China
ZHU Danning, East China Jiaotong University, Nanchang, Jiangxi 330013, China;Institute of Optical and Electrical Machinery Technology and Application, Nanchang, Jiangxi 330013, China
WU Mingming, East China Jiaotong University, Nanchang, Jiangxi 330013, China;Institute of Optical and Electrical Machinery Technology and Application, Nanchang, Jiangxi 330013, China
SUN Xudong, East China Jiaotong University, Nanchang, Jiangxi 330013, China;Institute of Optical and Electrical Machinery Technology and Application, Nanchang, Jiangxi 330013, China
ZHANG Zhicheng, East China Jiaotong University, Nanchang, Jiangxi 330013, China;Institute of Optical and Electrical Machinery Technology and Application, Nanchang, Jiangxi 330013, China

Abstract

The feasibility was investigated for online detection of soluble solids content (SSC) of “Yuluxiang” pear by visible-near infrared (visible-NIR) diffuse transmittance spectroscopy. 358 samples were divided into the calibration and prediction sets (269∶89) for developing calibration models and assessing their performance. By analyzing, the Vis-NIR transmission spectra of 'YuLuxiang' pears have three peaks at 625 nm, 725 nm and 800 nm and three troughs at 625 nm, 725 nm and 800 nm, respectively. Different preprocessing approaches were tested, it was found that the best approaches were the combination of first derivative (1D), smoothing and multiplicative scattering correction (MSC) preprocessing methods. The partial least square (PLS) regression and least square support vector machine (LS-SVM) models were developed with the pretreatment methods by the combination of first derivative (1D), smoothing and multiplicative scattering correction (MSC). The new samples of the prediction set were applied to evaluate the performance of the models. Compared with PLS model, the performance of LS-SVM model was better with the root mean square error of prediction (RMSEP) of 0.316% and the correlation coefficient of prediction of 0.949. And the spectral dimension reduction method of principal component analysis (PCA) and the kernel function of radial basis function (RBF) were suitable to improve the predictive ability of the LS-SVM model. The results suggested that it was feasible for online detection of SSC of ‘Yuluxiang’ pear by visible-NIR diffuse transmission spectroscopy combined with LS-SVM algorithm. The online detection of soluble solids content (SSC) of “Yuluxiang” pear by visible-near infrared (visible-NIR) diffuse transmittance spectroscopy was demonstrated.

Publication Date

10-28-2016

First Page

115

Last Page

119,163

DOI

10.13652/j.issn.1003-5788.2016.10.028

Recommended Citation

Yande, LIU; Danning, ZHU; Mingming, WU; Xudong, SUN; and Zhicheng, ZHANG (2016) "Online detection of soluble solids contents for “Yuluxiang” pear by visible-near infrared diffuse transmission spectroscopy," Food and Machinery: Vol. 32: Iss. 10, Article 27.
DOI: 10.13652/j.issn.1003-5788.2016.10.028
Available at: https://www.ifoodmm.cn/journal/vol32/iss10/27

References

［1］ WANG Ai-chen, XIE Li-juan. Technology using near infrared spectroscopic and multivariate analysis to determine the soluble solids content of citrus fruit ［J］. Journal of Food Engineering, 2014, 143: 17-24.
［2］ 王蒙, 冯晓元. 梨果实近红外光谱无损检测技术研究进展［J］. 食品质量安全检测学报, 2014, 5(3): 681-690.
［3］ LEMBE S Magwaza, UMEZURUIKE Linus Opara, HLNE Nieuwoudt, et al. NIR spectroscopy applications for internal and external quality analysis of citrus fruit: a review［J］. Food Bioprocess Technol, 2012, 5(2): 425-444.
［4］ 朱丽伟, 马文广, 胡晋. 近红外光谱技术检测种子质量的应用研究进展［J］. 光谱学光谱分析, 2015, 35(2): 346-349.
［5］ 孙通, 江水泉. 基于可见近红外光谱和变量优选的南水梨糖度在线检测［J］. 食品与机械, 2016, 32(3): 69-72.
［6］ VIEGAS T R, MATA A L M L, DUARTEM M L, et al. Determination of quality attributes in wax jambu fruit using NIRS and PLS［J］. Food Chemistry, 2016, 190: 1-4.
［7］ 王伟明, 董大明, 郑文刚, 等. 梨果糖浓度近红外漫反射光谱检测的预处理方法研究［J］. 光谱学光谱分析, 2013, 33(2): 359-362
［8］ 徐惠荣, 陈晓伟, 应义斌. 基于多元校正法的香梨糖度可见/近红外光谱检测［J］. 农业机械学报, 2010, 41(12): 1 000-1 298.
［9］ 许文丽, 孙通, 胡田, 等. 基于变量优选和快速独立成分分析的黄花梨可溶性固形物可见／近红外光谱检测［J］. 光谱学光谱分析, 2014, 34(12): 3 253-3 256.
［10］ 许禄, 邵学广. 化学计量学方法［M］. 2版. 北京: 科学出版社, 2004: 10-38.
［11］ 刘雪梅, 章海亮. 基于DPLS 和LS-SVM 的梨品种近红外光谱识别［J］. 农业机械学报, 2012， 43(9): 1 000-1 298.
［12］ 褚小立. 化学计量学方法与分子光谱分析技术［M］. 北京: 化学工业出版社, 2011: 41-53.
［13］ 尼珍, 胡昌勤, 冯芳. 近红外光谱分析中光谱预处理方法的作用及其发展［J］. 药物分析杂志, 2008, 28(5): 824-829
［14］ 张欣, 单杨, 李水芳. 基于多元散射校正和偏最小二乘 (MSC/PLS) 的傅里叶变换近红外光谱检测蜂蜜中还原糖［J］. 食品与机械, 2009, 25(6): 109-112.
［15］ 陆辉山, 陈鹏强, 闫宏伟, 等. 基于近红外光谱漫透射技术的苹果可溶性固形物含量在线检测［J］. 食品与机械, 2015, 31(3): 40-42.
［16］ 王加华, 韩东海. 便携式近红外水果糖度分析模型简化研究［J］. 食品安全质量检测技术, 2009, 1(1): 32-38.
［17］ 严衍禄. 近红外光谱分析基础与应用［M］. 北京: 中国轻工业出版社, 2005: 8-39.