A FT-NIR Process Analytical Technology Approach for Milk Renneting Control

Grassi, Silvia; Strani, Lorenzo; Alamprese, Cristina; Pricca, Nicolò; Casiraghi, E.; Cabassi, Giovanni

doi:10.3390/foods11010033

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Fourier Transformed (FT)-NIR has become popular for online and at-line process control in the dairy industry with many practical applications such as the determination of moisture, protein, fat and lactic acid ( 41 , 42 ). FT-NIR has also been used by Grassi et al ( 43 ) to monitor milk coagulation after the addition of rennet. The study found that an FT-NIR probe could successfully monitor coagulation in real time when combined with multivariate curve resolution and alternating least squares for data analysis.…”

Section: Spectroscopic Technologies and Their Usage In The Dairy Indu...mentioning

confidence: 99%

Spectroscopic technologies and data fusion: Applications for the dairy industry

et al. 2023

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Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

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Section: Spectroscopic Technologies and Their Usage In The Dairy Indu...mentioning

confidence: 99%

Spectroscopic technologies and data fusion: Applications for the dairy industry

et al. 2023

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“…For this reason, different types of sensors that can provide timely information are becoming more and more used for on-line quality probing from raw materials to the semi-finished and final products. It has been widely demonstrated that NIR spectroscopy has a powerful potential in monitoring food production processes [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], due to its ability to detect both chemical and physical changes in the samples. To cite a few applications: NIR has been used for process monitoring in the dairy industry, from the prediction of raw milk composition to milk coagulation in cheese production and yogurt fermentation [ 11 ]; the fermentation processes in the wine and brewery industries; and the powdered ingredients mixing stage in different food matrices [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

Tanzilli

D’Alessandro

Tamelli

et al. 2023

Foods

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The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.

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“…Then, the calibration set was chosen by using the Kennard-Stone algorithm and next, all method performances were compared by evaluating the validation sample set at multiple process stages (from the loading dock to the process line), which is a critical part of PAT initiatives. [7][8][9][10] Handheld, portable, and miniaturised NIR and Raman instruments are especially suited for PAT applications. In fact, their portability and versatility make them suitable for monitoring the pharmaceutical process.…”

Section: Introductionmentioning

confidence: 99%

Portable near‐infrared and Raman spectroscopic devices as complementary tools for instantaneous quality control of turmeric powder

Cruz¹,

Khongkaew

Bertotto

et al. 2023

Phytochemical Analysis

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Introduction Process analytical technology (PAT) guidance is implemented in the quality assurance of phytocompounds to achieve the Industry 4.0 concept. Near‐infrared (NIR) and Raman spectroscopies are feasible for rapid, reliable quantitative analysis through transparent packaging without removing the samples from their original containers. These instruments can serve PAT guidance. Objective This study aimed to develop online portable NIR and Raman spectroscopic methods for quantifying total curcuminoids in turmeric samples through a plastic bag. The method mimicked an in‐line measurement mode in PAT compared with placing samples into a glass vessel (at‐line mode). Materials and methods Sixty‐three curcuminoid standard‐spiked samples were prepared. Then, 15 samples were randomly selected as fixed validation samples, and 40 of the 48 remaining samples were chosen as calibration set. The results obtained from the partial least square regression (PLSR) models constructed by using the spectra acquired from NIR and Raman were compared with the reference values from high‐performance liquid chromatography (HPLC). Results The optimum PLSR model of at‐line Raman was achieved with three latent variables and a root mean square error of prediction (RMSEP) of 0.46. Meanwhile, the PLSR model of at‐line NIR with one latent variable offered an RMSEP of 0.43. For the in‐line mode, PLSR models created from Raman and NIR spectra had one latent variable with RMSEP of 0.49 and 0.42, respectively. The R2 values for prediction were 0.88–0.92. Conclusion The models established from the spectra from portable NIR and Raman spectroscopic devices with the appropriate spectral pretreatments allowed the determination of total curcuminoid contents through plastic bag.

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A FT-NIR Process Analytical Technology Approach for Milk Renneting Control

Cited by 11 publications

References 27 publications

Spectroscopic technologies and data fusion: Applications for the dairy industry

Spectroscopic technologies and data fusion: Applications for the dairy industry

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

Portable near‐infrared and Raman spectroscopic devices as complementary tools for instantaneous quality control of turmeric powder

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