Feasibility of pocket-sized near-infrared spectrometer for the prediction of cheese quality traits

Manuelian, Carmen L.; Ghetti, Matteo; Lorenzi, Claudia De; Pozza, Marta; Franzoi, Marco; Marchi, Massimo De

doi:10.1016/j.jfca.2021.104245

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…NIR spectroscopy is, therefore highly suitable for predicting main chemical components like fat, protein, and moisture, and it has also been proved to be effective for monitoring the compliance of nutritional labels with EU tolerance limits of a wide varety of food products ( 40 ). However, it is not stable for predicting chemicals with small amounts in cheese products [e.g., minerals, fatty acids; ( 41 )] since the amount of chemicals can alter the accuracy of prediction. NIR has a great degree of spectrum stability, which has made it quite popular at the industry level ( 42 ).…”

Section: Resultsmentioning

confidence: 99%

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses

Stocco,

Gómez-Mascaraque,

Deshwal

et al. 2024

Front. Nutr.

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The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950–1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses.

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Section: Resultsmentioning

confidence: 99%

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses

Stocco,

Gómez-Mascaraque,

Deshwal

et al. 2024

Front. Nutr.

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“…When comparing the accuracy with a benchtop instrument, however, no substantial difference in accuracy resulted. The NIR miniaturization can consequently be an opportunity in dairy industry ( Manuelian et al, 2022 ). Bittante et al published an invited review with focus on VIS and NIR spectroscopies to predict the cheese chemical composition ( Bittante et al, 2022 ).…”

Section: Applications To Food Quality Characterization and Adulterationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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“…However, to determine other adulterants, unrelated to milk greater subregions should be used, such as water, urea, and bovine whey as in the findings of da Paixão Teixeira et al [9], alongside cellulose, silicon dioxide, wheat flour, wheat semolina, and sawdust as documented by Visconti et al [22]. Similarly, by using a pocket-sized NIR system, Manuelian et al [21] studied chemical parameters such as total nitrogen, soluble nitrogen, ripening index, major minerals content (Ca, K, Mg, Na, and P), and fatty acid profile, proving that the performance was comparable to benchtop devices and confirming the potential for a breakthrough of NIR technology in the dairy industry. Commercial grated cheeses Detection of adulterants, i.e., cellulose, silicon dioxide, wheat flour, wheat semolina, and sawdust in grated cheese 4000-5000 and 6000-7000 cm −1 / 2500-2000, 1667-1429 nm [22] Soft and semi-hard cheeses (Caciotta, Mozzarella, Mozzarella Pizza, Primo sale, Ricotta siero, and Scarmorza)…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the fingerprinting and profiling methods including spectroscopic techniques and e-sensors, in combination with chemometrics, are deemed more suitable for determining authenticity, with marker-based methods such as MS techniques reserved for more precise adulteration detection [13]. However, the advancement of spectroscopic techniques, their instrumentation, and algorithmic means of analyses continues apace and offers the portable capability and, in some circumstances, even a viable lab-based alternative to MS. Vibrational spectroscopy techniques, particularly near-infrared spectroscopy (NIR), a popular technique for many years in food analysis, and combination with chemometric tools have given rise to many recent research studies [5,[18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Pre-Trial Study to Identify Species of Origin in Halloumi Cheese Utilising Chemometrics with Near-Infrared and Hyperspectral Imaging Technologies

Tarapoulouzi,

Logan,

Hardy

et al. 2024

Analytica

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Halloumi cheese has recently gained a Protected Designation of Origin (PDO) indicator, which is related to the place (Cyprus) in which halloumi cheese is made. The PDO label is linked with several requirements, e.g., milk species, quantities, etc.; thus, it is important to study this product regarding authenticity. The utility of using two spectroscopic techniques, hyperspectral imaging (HSI) (400–1000 nm) and conventional near-infrared spectroscopy (NIR) (800–2500 nm) were assessed for the discrimination of 17 Cypriot halloumi cheese types, which could be categorized as of cow or goat–sheep origin. The aim of this study was to obtain spectral information for halloumi cheese using other promising infrared and imaging spectroscopic techniques as a comparison to a previously acquired mid-infrared (MIR) spectroscopy dataset. NIR and HSI are both fast and easy techniques in application, both of which provide significant information in food analysis. Chemometric analysis was crucial for interpreting the spectroscopic data by applying the unsupervised methods: principal component analysis (PCA) and hierarchical cluster analysis (HCA). The HSI model was found to be based intuitively on the appearance of cheese samples after freeze-drying (e.g., color; yellow/white, and texture; oily/dry), while the NIR grouping of samples was determined to be based on composition, mainly fat, protein and lactose content of the cheese samples. The HSI model returned distinct clusters of the two halloumi cheese types, cow and goat–sheep origin, with one outlier (16/17 accuracy; 94%), while the NIR model proved less accurate (13/17; 76%).

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Feasibility of pocket-sized near-infrared spectrometer for the prediction of cheese quality traits

Cited by 10 publications

References 22 publications

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses

Portable NIR spectroscopy: the route to green analytical chemistry

A Pre-Trial Study to Identify Species of Origin in Halloumi Cheese Utilising Chemometrics with Near-Infrared and Hyperspectral Imaging Technologies

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