Food Technology, University of Salamanca Escuela Politécnica Superior de Zamora, Avenida Requejo 33, 49022 Zamora, Spain.
Analytical Chemistry, Nutrition and Bromatology, University of Salamanca Calle Plaza de los Caídos s/n, 37008 Salamanca, Spain.
Sensors (Basel). 2020 Dec 2;20(23):6892. doi: 10.3390/s20236892.
For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat "cecina de León", a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.
对于受保护地理标志（PGI）标签产品，如风干牛肉“塞西娜·德·莱昂”（Cecina de León），必须进行感官分析。然而，这是一个复杂且耗时的过程。本研究探索了使用近红外光谱（NIRS）结合人工神经网络（ANN）预测感官属性的可行性。记录了 50 个塞西娜样本的光谱，获得了 451 个反射率数据。对于每个感官参数，优化了具有 451 个神经元的前馈多层感知器 ANN，在隐藏层中神经元数量在 1 到 30 之间变化，以及单个神经元在输出层。当比较预测值和参考值时，获得的回归系数平方（RSQ > 0.8，除了气味强度外）和预测误差均方根（MSEP）值表明，准确预测 24 个感官参数中的 23 个是可能的。尽管只有 3 个感官参数显示 PGI 和非 PGI 样本之间存在显著差异，但应用于 NIR 光谱的优化 ANN 架构实现了 100%样本的正确分类，而残差均方根法（RMS-X）允许区分 100%的非 PGI 样本。
