Abstract
Spectra data of the 101 loquat samples were obtained by using NIRS and the TSS, titratable acid, Vc of the samples were determined by conventional chemical methods. The mathematical pretreatment methods were used as spectral preprocessing options, and then the calibration models of TSS, titratable acid and Vc were established respectively by PLS. By using these models, the TSS, titratable acid and Vc in the validation sets can be predicted. The Rp2 are 0.906, 0.745, 0.554, RMSEP are 0.628, 0.048, 2.23 respectively, and the RPD of TSS is 3.31. Thus, the calibration model of TSS can be used for the actual detection. The RPD of titratable acid and Vc are 2 and 1.52 respectively. The content of titratable acid and Vc in the loquat can be tested by NIRS. However, the detection accuracy remains to be improved.
Publication Date
5-28-2016
First Page
67
Last Page
70,97
DOI
10.13652/j.issn.1003-5788.2016.05.015
Recommended Citation
Xi, WANG; Xiyu, WU; Lan, PANG; and Dan, XU
(2016)
"Nondestructive detection of internal quality of loquat by near infrared spectroscopy,"
Food and Machinery: Vol. 32:
Iss.
5, Article 15.
DOI: 10.13652/j.issn.1003-5788.2016.05.015
Available at:
https://www.ifoodmm.cn/journal/vol32/iss5/15
References
［1］ 应义斌, 刘燕德. 水果内部品质光特性无损检测研究及应用［J］. 浙江大学学报: 农业与生命科学版, 2003, 29(2): 125-129.
［2］ 刘燕德, 应义斌, 傅霞萍. 近红外漫反射用于检测苹果糖度及有效酸度的研究［J］. 光谱学与光谱分析, 2005, 25(11): 1 793-1 796.
［3］ 孙素琴, 汤俊明, 袁子民, 等. 道地山药红外指纹光谱和聚类分析的鉴别研究［J］. 光谱学与光谱分析, 2003, 3(2): 258-261.
［4］ 何勇, 李晓丽, 邵咏妮. 基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究［J］. 光谱学与光谱分析, 2006, 26(5): 850-853.
［5］ Peiris K H S, Dull G G, Leffler R G, et al. Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers: Impications for the development and use of NIR spectrometric technique［J］. HortScience, 1999, 34(1): 114-118.
［6］ 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.
［7］ Peirs A, Lammertyn J, Ooms K, et al. Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR spectroscopy［J］. Postharvest Biology and Technology, 2001, 21(2): 189-199.
［8］ Slaughter D C. Nondestructive determination of internal quality in peaches and nectarines［J］. Transactions of the ASAE, 1995, 38(2): 617-623.
［9］ 陈香维. 猕猴桃近红外光谱无损检测技术研究［D］. 陕西: 西北农林科技大学, 2009: 119-129.
［10］ Schmilovitch Z, Mizrach A, Hollmun A, et al. Determination of mango physiological indices by near-infrared spectraometry［J］. Postharvest Biology and Technology, 2000, 5(19): 245-252.
［11］ 李东华, 纪淑娟, 重腾和明. 南国梨糖、酸度近红外光谱模型适用的贮藏研究［J］. 农业工程学, 2009, 25(4): 270-275.
［12］ 李建平, 傅霞萍, 周莹, 等. 近红外光谱定量分析技术在枇杷可溶性固形物无损检测中的应用［J］. 光谱学与光谱分析, 2006, 26(9): 1 605-1 609.
［13］ Fu Xia-ping, Li Jian-ping, Zhou Ying, et al. Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy［J］. Journal of Zhejiang University Science B, 2009, 10(2): 120-125.
［14］ Fu Xia-ping, Ying Yi-bin, Zhou Ying, et al. Application of probabilistic neural networks in qualitative analysis of near infrared spectra: Determination of producing area and variety of loquats［J］. Analytica Chimica Acta, 2007, 598(1): 27-33.
［15］ 王元秀, 庄海燕. 微量滴定法测定猕猴桃中维生素C的含量［J］. 济南大学学报: 自然科学版, 2001, 15(4): 374-375.
［16］ 闵顺耕, 李宁, 张明祥. 近红外光谱分析中异常值的判别与定量模型优化［J］. 光谱学与光谱分析, 2004, 24(10): 1 205-1 209.
［17］ Bedrick E. Graphical modelling and the mahalanobis distance［J］. Journal of Applied Statistics, 2005, 32(9): 959-967.
［18］ 牛智有, 韩鲁佳, 苏晓鸥, 等. 鱼粉中肉骨粉含量的近红外反射光谱分析［J］. 农业机械学报, 2006, 37(8): 126-129.
［19］ Malley DF, Rnicke H, Findlay D, et al. Feasibility of using near-infrared reflectance spectroscopy for the analysis of C, N, P and diatoms in lake sediments［J］. Journal of Paleolimnology, 1999, 21(3): 295-306.