基于近红外光谱技术的小龙虾新鲜度快速检测研究

小龙虾是近年来广受消费者欢迎的淡水产品，相关产业迅猛发展，产生巨大的经济利益。小龙虾整虾及虾仁、虾尾在运输过程中极易腐败变质，产生有害物质。如果不能对小龙虾新鲜度进行及时检测，任由腐败小龙虾进入食品流通环节，极易酿成食品安全事故，危害消费者生命安全，对整个产业链造成不良影响。挥发性盐基氮(TVBN)是衡量水产品新鲜度的主要指标，也可以用于衡量小龙虾的新鲜度，但传统的挥发性盐基氮检测方法存在步骤复杂、检测时间长和化学试剂污染的等问题，无法满足小龙虾庞大产业链的检测需求。近红外光谱技术是一种快速、无损、环境友好的分析技术，在食品分析领域中已有较为广泛的应用。本研究基于近红外光谱分析技术(NIR),结合化学计量学方法提出一种小龙虾新鲜度的快速检测方法。使用偏最小二乘算法(PLS)建立小龙虾虾尾挥发性盐基氮定量分析模型。为了提高模型的预测能力，使用多元散射校正(MSC)、标准正态变换(SNV)、连续小波变换(CWT)和1阶导数(1st)法对光谱进行预处理，扣除光谱背景；使用蒙特卡洛-无信息消除(MC-UVE)和随机检测(RT)算法进行波长筛选，选择光谱中有效变量。结果显示，光谱预处理和波长筛选...

Fast Evaluation of Freshness in Crayfish (Prokaryophyllus clarkii) Cased on Near-Infrared Spectroscopy

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.