以番茄可溶性固形物含量（SSC）的无损速测为例， 分别采用线性渐变分光（LVF）、 数字光处理（DLP）近红外光谱仪对大、 小番茄采集近红外光谱数据; 分别基于两种近红外光谱仪数据计算大、 小番茄平均光谱及差谱， 并比较两种近红外光谱仪所采集大、 小番茄近红外光谱数据的特征; 对两种近红外光谱仪的数据分别进行主成分分析（PCA）， 并比较了大、 小番茄前3主成分的得分分布; 按SSC梯度对数据进行分级， 采用偏最小二乘（PLS）回归结合全交互验证算法分别基于两种近红外光谱仪数据建立番茄SSC定量校正模型。 结果表明: （1）大、 小番茄LVF近红外光谱的平均光谱及其差谱的光谱特征分别与DLP近红外光谱的平均光谱及其差谱的光谱特征相似。 （2）大、 小番茄LVF近红外光谱数据PCA前3主成分得分散点分离趋势不明显， 而DLP近红外光谱数据PCA前3主成分得分散点基本上不具有分离趋势。 （3）基于LVF近红外光谱数据所建各模型的相对预测性能（RPD）皆不低于2.11， 其中标准化预处理所建模型具有最佳性能， 模型维数（Nf）、 校正测定系数（R2C）、 校正均方根误差（RMSEC）、 交互验证测定系数（R2CV）、 交互验证均方根误差（RMSECV）、 RPD、 预测相关系数（RP）、 预测均方根误差（RMSEP）分别为8、 0.949 1、 0.27、 0.899 9、 0.38、 3.16、 0.882 6、 0.63; 基于DLP近红外光谱数据所建各模型的RPD皆不低于1.60， 其中标准化预处理所建模型具有最佳性能， Nf、 R2C、 RMSEC、 R2CV、 RMSECV、 RPD、 RP、 RMSEP分别为5、 0.823 5、 0.49、 0.728 6、 0.62、 1.94、 0.788 4、 0.80。 该研究可为番茄SSC的无损快速测定以及果蔬品质无损快速检测的仪器选择与评价提供一定的参考。

In this thesis, it took the non-destructive rapid testing of solid soluble content (SSC) in tomatoes as example. The near-infrared (NIR) spectra data of big and small tomatoes were collected by linear variable filter (LVF) NIR spectrometer and digital light processing (DLP) NIR spectrometer respectively. The average NIR spectra of big and small tomatoes and the difference spectra were calculated for LVF and DLP spectra respectively. The characteristics of the NIR spectra data of the two types of tomatoes collected by LVF and DLP spectrometer were compared respectively. Principal component analysis (PCA) was done on the LVF and DLP spectra respectively, and the distribution of the scores of the first 3 principal components were compared. The data were divided into calibration and external validation sets according to the SSC gradient. Partial least squares regression combined with a full cross-validation algorithm was applied to develop the quantitative calibration models of SSC in tomato for the spectra data collected by LVF and DLP spectrometer respectively. It is demonstrated by the result that: (1) The spectral characteristics of the average spectra and difference spectra of LVF-NIR spectra of big and small tomatoes are similar to those of DLP-NIR spectra, which indicates that it is feasible to carry out non-destructive and rapid testing of SSC in tomato by the LVF and DLP NIR spectrometers. (2) The separation trend of the score scatters of the first 3 principal components of LVF-NIR spectral data of big and small tomatoes was not obvious, while there is little separation trend for that of DLP-NIR spectral data. (3) The ratio performance deviation (RPD) values of the models developed by the LVF-NIR spectral data were no less than 2.11. Among them, the preprocessing of normalization acquired the optimized model, of which the number of factors (Nf), determination of calibration (R2C), root mean square error of calibration (RMSEC), determination of cross validation (R2CV), root mean square error of cross validation (RMSECV), RPD, correlation coefficient of prediction (rP) and root mean square error of prediction (RMSEP) were 8, 0.949 1, 0.27, 0.899 9, 0.38, 3.16, 0.882 6 and 0.63 respectively. The RPD values of the models developed by the DLP-NIR spectral data were no less than 1.60. Among them, the preprocessing of normalization acquired the optimized model, of which the Nf, R2C, RMSEC, R2CV, RMSECV, RPD, RP and RMSEP were 5, 0.823 5, 0.49, 0.728 6, 0.62, 1.94, 0.788 4 and 0.80 respectively. This thesis will, to some extent, provide reference to the non-destructive and rapid testing of SSC in tomatoes and the selection and evaluation of the non-destructive and rapid instrument for testing the quality of fruits and vegetables.