Fast Evaluation of Freshness in Crayfish (Prokaryophyllus clarkii) Cased on Near-Infrared Spectroscopy
WANG Chao1, LIU Yan1*, XIA Zhen-zhen2, WANG Qiao1, DUAN Shuo1
1. College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
2. Institute of Agricultural Quality Standards and Testing Technology Research, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
Abstract:Crayfish is one of the most popular freshwater products in China. The industrial chain of crayfish has rapidly developed and produced gorgeous economic benefits. Easy to be putrid during the logistics transportation, the freshness of crayfish and related products must be monitored and has paid much attention in recent years. If the putrid crayfish cannot be detected in time, food safety accidents may happen, and the whole industrial chain of crayfish would be destroyed. The total volatile basic nitrogen (TVBN) is the common index of freshness for aquatic products and can be used to evaluate the freshness of crayfish. The traditional analytical methods for TVBN are accurate but complex, time-consuming and environmentally hazardous. Developing novel, fast and stable methods are inevitable for the freshness evaluation of crayfish with large scale. Near-infrared spectroscopy (NIR) is a fast, non-destructive and environmentally friendly analytical technique widely used in many fields. In this study, a method for monitoring the freshness of crayfish by near-infrared spectroscopy combined with chemometrics was proposed. The TVBN were adopted as the freshness index and the quantitative models were built by partial least squares (PLS). The spectral pretreatment and variable selection methods were adopted to improve the models further. For the edible part of the crayfish, reasonable validation results can be obtained by using the optimized models. The combination of 1st and (MC-UVE) seems to have the better optimization results. For total volatile basic nitrogen (TVBN), the root means square error of prediction (RMSEP) and correlation coefficient (r) of the crayfish tails were 1.626 and 0.950.
Key words:Near-infrared spectroscopy; Crayfish; Total volatile basic nitrogen; Partial least squares
王 超,刘 言,夏珍珍,王 桥,段 烁. 基于近红外光谱技术的小龙虾新鲜度快速检测研究[J]. 光谱学与光谱分析, 2023, 43(01): 156-161.
WANG Chao, LIU Yan, XIA Zhen-zhen, WANG Qiao, DUAN Shuo. Fast Evaluation of Freshness in Crayfish (Prokaryophyllus clarkii) Cased on Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 156-161.
[1] CHU Xiao-li, CHEN Pu, LI Jing-yan, et al(褚小立,陈 瀑,李敬岩,等). Journal of Instrumental Analysis(分析测试学报), 2020, 39(10): 1181.
[2] ZHANG Jin, HU Yun, ZHOU Luo-xiong, et al(张 进,胡 芸,周罗雄,等). Journal of Instrumental Analysis(分析测试学报), 2020, 39(10): 1196.
[3] Hu Y J, Zhang H J, Liang W Q, et al. Journal of AOAC International, 2020, 103(2): 504.
[4] Liu Y, Xia Z Z, Yao L Y, et al. Journal of Food Composition and Analysis, 2019,(84): 103327.
[5] Wang S Y, Zhang J, Cai W S, et al. Chinese Chemical Letters, 2019, 30(5): 1024.
[6] Muncan J, Matovic V, Nikolic S, et al. Talanta, 2020, 206: 120253.
[7] Wang L, Sun D W, Pu H B, et al. Critical Reviews in Food Science and Nutrition, 2017, 57(7): 1524.
[8] Prieto N, Pawluczyk O, Dugan M E R, et al. Applied Spectroscopy, 2017, 71(7): 1403.
[9] Zareef M, Chen Q, Hassan M M, et al. Food Engineering Reviews, 2020, 12: 173.
[10] Grassi S, Alamprese C. Current Opinion in Food Science, 2018, 22: 17.
[11] Kucha C T, Ngadi M. Journal of Food Measurement and Characterization, 2020, 14: 1105.
[12] Xiong Y, Ohashi S, Nakano K, et al. Journal of Food Engineering, 2020, 298: 110417.
[13] Arlot S, Lerasle M. The Journal of Machine Learning Research, 2016, 17(1): 7256.
[14] Liu Y, Wang Y X, Xia Z Z, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 211: 336.