Abstract Near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques in tandem with chemometric analyses were employed for developing multispectral real-time systems allowing the food industry to monitor moisture content (MC) in tubers including various potato and sweet potato products during drying. Multivariate models were established by partial least-squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and back propagation artificial neural network (BPANN) using full spectral ranges of 10372–6105 cm−1 (Spectral Set I), 3996–600 cm−1 (Spectral Set II), and 1700–900 cm−1 (Spectral Set III). The LWPLSR from Spectral Set I and BPANN from Spectral Set II and III, obtained the highest accuracies for tuber MC prediction. Then, both regression coefficient (RC) and successive projection algorithm (SPA) were respectively used for the selection of feature wavelengths in Spectral Set I, II and III. Instead of choosing many groups of characteristic variables for different varieties of potatoes and sweet potatoes, one set of feature variables for all tubers was selected from each spectral region for the convenience of industrial application. Eventually, six sets of feature wavelengths chosen from Spectral Set I, II and III were used to optimize models. The simplified SPA-LWPLSR from Spectral Set II and SPA-BPANN from Spectral Set III acquired good model performances for the tuber MC prediction, with determination coefficients in prediction (R2P) of 0.950 and 0.904, respectively. The RC-BPANN model from Spectral Set I achieved the highest R2P of 0.965. Such accuracies were comparable to that of full spectral models. The results reveal that hyperspectral techniques have great potential in the food industry for real-time measurement of tuber MC.

中文翻译：

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使用近/中红外 (NIR/MIR) 高光谱技术在热空气和微波干燥过程中化学测定马铃薯和甘薯块茎中的时间序列水分

摘要 近红外 (NIR) 和中红外 (MIR) 高光谱技术与化学计量分析相结合，用于开发多光谱实时系统，使食品工业能够监测块茎（包括各种马铃薯和甘薯产品）的水分含量 (MC)在干燥过程中。通过偏最小二乘回归 (PLSR)、支持向量机回归 (SVMR)、局部加权偏最小二乘回归 (LWPLSR) 和反向传播人工神经网络 (BPANN) 使用 10372–6105 cm 的全光谱范围建立多元模型−1（光谱组 I）、3996–600 cm−1（光谱组 II）和 1700–900 cm−1（光谱组 III）。来自光谱集 I 的 LWPLSR 和来自光谱集 II 和 III 的 BPANN 获得了块茎 MC 预测的最高准确度。然后，在光谱集I、II和III中分别使用回归系数(RC)和逐次投影算法(SPA)选择特征波长。为方便工业应用，不是为不同品种的马铃薯和红薯选择多组特征变量，而是从每个光谱区域中为所有块茎选择一组特征变量。最终，从光谱集 I、II 和 III 中选择的六组特征波长用于优化模型。来自光谱集 II 的简化 SPA-LWPLSR 和来自光谱集 III 的 SPA-BPANN 获得了良好的块茎 MC 预测模型性能，预测中的决定系数 (R2P) 分别为 0.950 和 0.904。来自 Spectral Set I 的 RC-BPANN 模型实现了 0.965 的最高 R2P。这种精度与全光谱模型的精度相当。结果表明，高光谱技术在食品行业实时测量块茎 MC 方面具有巨大潜力。