Abstract Parmigiano Reggiano (P-R) is one of the most important Italian food products labelled with Protected Designation of Origin (PDO). The PDO denomination is applied also to grated P-R cheese products meeting the requirements regulated by the Specifications of Parmigiano Reggiano Cheese. Different quality parameters are monitored, including the percentage of rind, which is edible and should not exceed the limit of 18% (w/w). The present study aims at evaluating the possibility of using near infrared hyperspectral imaging (NIR-HSI) to quantify the rind percentage in grated Parmigiano Reggiano cheese samples in a fast and non-destructive manner. Indeed, NIR-HSI allows the simultaneous acquisition of both spatial and spectral information from a sample, which is more suitable than classical single-point spectroscopy for the analysis of heterogeneous samples like grated cheese. Hyperspectral images of grated P-R cheese samples containing increasing levels of rind were acquired in the 900–1700 nm spectral range. Each hyperspectral image was firstly converted into a one-dimensional signal, named hyperspectrogram, which codifies the relevant information contained in the image. Then, the matrix of hyperspectrograms was used to calculate a calibration model for the prediction of the rind percentage using Partial Least Squares (PLS) regression. The calibration model was validated considering two external test sets of samples, confirming the effectiveness of the proposed approach.

中文翻译：

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探索 NIR 高光谱成像在磨碎的 Parmigiano Reggiano 奶酪中自动量化果皮量的潜力

摘要 Parmigiano Reggiano (PR) 是最重要的意大利食品之一，贴有受保护的原产地名称 (PDO)。PDO 名称也适用于满足 Parmigiano Reggiano Cheese 规范规定要求的磨碎 PR 奶酪产品。监测不同的质量参数，包括可食用的外皮百分比，不得超过 18% (w/w) 的限制。本研究旨在评估使用近红外高光谱成像 (NIR-HSI) 以快速且无损的方式量化磨碎的 Parmigiano Reggiano 奶酪样品中果皮百分比的可能性。事实上，NIR-HSI 允许从样本中同时获取空间和光谱信息，这比经典的单点光谱更适合分析磨碎的奶酪等异质样品。在 900-1700 nm 光谱范围内获得了含有越来越多外皮的磨碎 PR 奶酪样品的高光谱图像。每张高光谱图像首先被转换为一维信号，称为高光谱图，它编码了图像中包含的相关信息。然后，使用高光谱图矩阵计算使用偏最小二乘法 (PLS) 回归预测果皮百分比的校准模型。考虑到两个外部样本测试集验证了校准模型，证实了所提出方法的有效性。在 900-1700 nm 光谱范围内获得了含有越来越多外皮的磨碎 PR 奶酪样品的高光谱图像。每张高光谱图像首先被转换为一维信号，称为高光谱图，它编码了图像中包含的相关信息。然后，使用高光谱图矩阵计算使用偏最小二乘法 (PLS) 回归预测果皮百分比的校准模型。考虑到两个外部样本测试集验证了校准模型，证实了所提出方法的有效性。在 900-1700 nm 光谱范围内获得了含有越来越多外皮的磨碎 PR 奶酪样品的高光谱图像。每张高光谱图像首先被转换为一维信号，称为高光谱图，它编码了图像中包含的相关信息。然后，使用高光谱图矩阵计算使用偏最小二乘法 (PLS) 回归预测果皮百分比的校准模型。考虑到两个外部样本测试集验证了校准模型，证实了所提出方法的有效性。高光谱图矩阵用于计算校准模型，用于使用偏最小二乘法 (PLS) 回归预测果皮百分比。考虑到两个外部样本测试集验证了校准模型，证实了所提出方法的有效性。高光谱图矩阵用于计算校准模型，用于使用偏最小二乘法 (PLS) 回归预测果皮百分比。考虑到两个外部样本测试集验证了校准模型，证实了所提出方法的有效性。