Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese. (June 2020)

• Record Type: Journal Article
• Authors: Calvini, R.; Michelini, S.; Pizzamiglio, V.; Foca, G.; Ulrici, A.
• Is Part Of: Food control. Volume 112(2020)
• Publication Date: 2020-06
• Subjects: Grated cheese -- Rind percentage -- NIR hyperspectral imaging -- Multivariate calibration -- Multivariate image analysis; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107111
• Languages: English
• ISSNs: 0956-7135

Abstract

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. Highlights: Rind percentage in grated Parmigiano Reggiano cheese should not exceed 18% (w/w). Hyperspectral imaging (HSI) was used to estimate rind percentage in grated cheese. The images were converted in one-dimensional signals named hyperspectrograms. The hyperspectrograms were used to build PLS calibration models. Validation with test samples confirmed the effectiveness of the proposed approach.