应用拉曼光谱技术无损快速检测四季豆新鲜度

Application of Raman Spectroscopy for Non-destructive and Rapid Detection of the Freshness of Green Bean

本研究提出一种基于拉曼光谱技术无损快速检测四季豆新鲜度的方法。通过采集在不同贮藏时间四季豆样本的拉曼光谱,同时检测可以指示其新鲜度变化的理化指标,包括质量损失率、总色差?E、硬度、抗坏血酸含量以及叶绿素含量,用化学计量学方法将四季豆光谱数据与理化指标值进行关联,实现对四季豆的无损快速检测。分别比较基线校正、高斯滤波、归一化、标准正态量变换等方法对拉曼光谱进行预处理的效果,建立四季豆新鲜度指标全波数和优选波数的偏最小二乘回归(partial least squares regression,PLSR)和主成分回归(principal component regression,PCR)模型。采用回归系数法优选拉曼光谱的特征波数,简化后各指标PLSR模型的校正集相关系数(r_(c))均大于0.92,预测集相关系数(r_(p))均大于0.89,剩余预测偏差均大于2.0;PCR模型的r_(c)均大于0.79,r_(p)均大于0.73。因此,本研究结果表明,应用拉曼光谱技术可实现四季豆新鲜度的无损快速检测。

This study proposed a non-destructive method for rapid freshness evaluation of green bean based on Raman spectroscopy.The Raman spectra of green bean with different storage times were collected,its physicochemical indicators reflecting changes in freshness including mass loss rate,total color difference(∆E),hardness,ascorbic acid content and chlorophyll content were measured,and chemometrics methods were used to associate the spectral data with the physicochemical data.The effectiveness of different spectral pretreatments such as baseline correction(BL),Gaussian filter(GF),normalization(NL)and standard normal variable(SNV)was compared,and partial least squares regression(PLSR)and principal component regression(PCR)were used individually to establish freshness prediction models will all or selected wavenumbers.Moreover,the regression coefficient method(RC)was used to select the characteristic Raman wavenumbers.The simplified PLSR model for each freshness indicator showed a correlation coefficient of calibration set(r_(c))of greater than 0.92,a correlation coefficient of prediction set(r_(p))of greater than 0.89,and a residual predictive deviation(RPD)of greater than 2.The r_(c) and r_(p) of the PCR model were greater than 0.79 and 0.73,respectively.Therefore,the results of this study indicate that Raman spectroscopy allows non-destructive and rapid detection of the freshness of green bean.