The possibility of using near infrared hyperspectral imaging spectroscopy to quantify the water content in commercial biscuits was investigated. Principal component analysis was successfully applied to hyperspectral images of commercial biscuits to monitor their water contents. Variables were selected and water contents quantified using analysis of variance, followed by multiple linear regression, and the results were compared with those obtained with variable importance in projection partial least squares. Initially equal to 212, the number of variables after application of analysis of variance was equal to 10. Analysis of variance–multiple linear regression gave better results: the coefficient of determination (R2) was higher than 0.92 and the root mean square error of validation was less than 0.015. The “prediction images” obtained were very relevant and can be used to study biscuit defects. The methodology developed could be implemented at the industrial level for biscuit quality control and for online monitoring of the uniform distribution of water in the superficial layer of biscuits.

中文翻译：

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使用近红外高光谱成像光谱和化学计量学预测饼干中的水分含量

研究了使用近红外高光谱成像光谱来量化商业饼干中的水含量的可能性。主成分分析已成功应用于商业饼干的高光谱图像以监测其含水量。选择变量并使用方差分析量化含水量，然后进行多元线性回归，并将结果与​​投影偏最小二乘法中变量重要性获得的结果进行比较。最初等于 212，应用方差分析后的变量数等于 10。 方差分析 - 多元线性回归给出了更好的结果：决定系数 (R2) 高于 0.92，验证的均方根误差小于 0.015。获得的“预测图像”非常相关，可用于研究饼干缺陷。开发的方法可以在工业层面实施，用于饼干质量控制和饼干表层水分均匀分布的在线监测。