Evaluation of the effect of factors related to preparation and composition of grated Parmigiano Reggiano cheese using NIR hyperspectral imaging. (January 2022)

• Record Type: Journal Article
• Authors: Calvini, R.; Michelini, S.; Pizzamiglio, V.; Foca, G.; Ulrici, A.
• Is Part Of: Food control. Volume 131(2022)
• Publication Date: 2022-01
• Subjects: NIR hyperspectral Imaging -- ANOVA Simultaneous component analysis -- Multivariate image analysis -- Data dimensionality reduction -- Parmigiano Reggiano cheese; 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.2021.108412
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: The present study is focused on the evaluation of the effect of grater type and fat content of the pulp on the spectral response obtained by near infrared hyperspectral imaging (NIR-HSI), when this technique is used to determine the rind percentage in Parmigiano Reggiano (P-R) cheese. To this aim, grated P-R cheese samples were prepared considering all the possible combinations between three levels of rind amount (8%, 18% and 28%), two levels of fat content of the pulp and two different grater types, and the corresponding hyperspectral images were acquired in the 900–1700 nm spectral range. In a first step, the average spectrum (AS) was calculated from each hyperspectral image, and the corresponding dataset was analysed by means of Analysis of Variance Simultaneous Component Analysis (ASCA) to assess the effect of the three considered factors and their two-way interactions on the spectral response. Then, the hyperspectral images were converted into Common Space Hyperspectrograms (CSH), which are signals obtained by merging in sequence the frequency distribution curves of quantities calculated from a Principal Component Analysis (PCA) model common to the whole hyperspectral image dataset. ASCA was also applied to the CSH dataset, in order to evaluate the effect of the considered factors on this kind of signals. Generally, all the three factors resulted to have a significant effect, but with a different extent according to the method used to analyse the hyperspectral images. Indeed, while fat content of the pulp and rind percentage showed a comparable effect on the spectral response of AS dataset, in the case of CSH signals rind percentage had a greater effect compared to the other main factors. However, CSH were also more sensitive to differences ascribable to the natural variability between diverse Parmigiano Reggiano cheese samples. Highlights: Rind amount in grated Parmigiano Reggiano (PR) cheese can be detected by NIR-HSI. NIR-HSI could be also influenced by other factors, mainly grater type and fat content. A multivariate study of the effect of these factors compared to rind amount was made. NIR-HSI data of grated PR cheese were reduced to signals and analysed by ASCA. ASCA confirmed the significant effect of the considered factors and of their interactions.