Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese

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 also 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, resulting 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.

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

“…In Figure 5, the contents of pizza are detected with hyperspectral imaging. The rotten sections can easily be detected in the hyperspectral image [43,44]. In the food industry, hyperspectral imaging can be used to count products [37].…”

Evaluation of the effect of factors related to preparation and composition of grated Parmigiano Reggiano cheese using NIR hyperspectral imaging

“…In the frame of the implementation of fast and non-destructive methods to assess quality compliance of cheese products (Alinovi et al, 2019;Cevoli et al, 2013;Lei & Sun, 2019;Li Vigni et al, 2020), in a previous study we evaluated the potential of Near Infrared Hyperspectral Imaging (NIR-HSI) in the determination of rind percentage in grated Parmigiano Reggiano cheese (Calvini, Michelini, et al, 2020). NIR-HSI allows to combine the advantages of NIR spectroscopy and of imaging techniques, to obtain spectral and spatial information from a sample.…”

“…In our previous study related to the application of NIR-HSI to determine rind percentage in grated P-R products, hyperspectral images of grated P-R cheese samples with varying levels of rind percentage were acquired in the 900-1700 nm spectral range (Calvini, Michelini, et al, 2020). The hyperspectral images were converted into the corresponding CSH signals, which were used to calculate a calibration model to estimate the RP values.…”

Prediction of Sensory Attributes of Fresh Beef Using Advanced Spectral Analysis

“…Installing computer-integrated imaging techniques helps in evaluating food units in individual basis or as a lot and provides relevant information about their characteristics and quality features. More recently, the applicability of imaging techniques is increasing in different agricultural areas due to the advancement occurred in modern computers and the availability of competent imaging devices (Vote et al, 2003;Shackelford et al, 2005;Calvini et al, 2020).…”