Laboratory of Analytical Chemistry & Bromatology, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco.
Laboratory of Chemical Processes and Applied Materials, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco.
J AOAC Int. 2021 Dec 11;104(6):1710-1718. doi: 10.1093/jaoacint/qsab068.
Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques.
We evaluated in this work the potential of mid-IR (MIR) spectroscopy and chemometric models to discriminate eight date palm varieties.
Four chemometric models were applied for the analysis of the spectral data, including principal-component analysis (PCA), support-vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA), and partial-least-squares (PLS) analysis. MIR spectroscopic data were recorded from the wavenumber range 4000-600 cm-1, with a spectral resolution of 4 cm-1.
The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. PLS analysis was applied as a complementary chemometric tool aimed at quantifying moisture content; the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root-mean-square error of cross-validation of 0.50.
The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content.
An alternative analytical method to discriminate date palm cultivars by FTIR-attenuated total reflection spectroscopy coupled with chemometric approaches is described.
摩洛哥是世界上重要的几种枣椰品种的生产国和消费国之一。事实上，品种之间的区分仍然很困难，需要使用复杂且昂贵的技术。
我们在这项工作中评估了中红外（MIR）光谱和化学计量学模型区分八种枣椰品种的潜力。
应用了四种化学计量学模型对光谱数据进行分析，包括主成分分析（PCA）、支持向量机判别分析（SVM-DA）、线性判别分析（LDA）和偏最小二乘（PLS）分析。MIR 光谱数据记录的波数范围为 4000-600 cm-1，光谱分辨率为 4 cm-1。
采用 LDA 和 SVM-DA 进行判别分析，对枣椰果肉的分类准确率达到 100%。PLS 分析作为一种补充化学计量工具，旨在定量测定水分含量；该模型的验证表明具有良好的预测能力，回归系数为 84%，交叉验证的均方根误差为 0.50。
本研究清楚地表明，MIR 光谱结合化学计量学方法构成了一种有前途的分析方法，可以根据品种来源对枣椰进行分类，并建立预测水分含量的回归模型。
本文描述了一种通过傅里叶变换衰减全反射（FTIR-ATR）光谱结合化学计量学方法来区分枣椰品种的替代分析方法。
