Process Analytical Technology in the food industry

Berg, Frans van den; Lyndgaard, Christian Bøge; Sørensen, Klavs Martin; Engelsen, Søren B.

doi:10.1016/j.tifs.2012.04.007

Cited by 95 publications

(50 citation statements)

References 14 publications

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“…38 Calibration transfer and maintenance to all the aforementioned studies were performed by augmenting the calibration model with only a few samples measured in the new secondary conditions. Data augmentation has been used in process analytical technology (PAT), [39][40][41][42] which is a manufacturing concept, originated from pharmaceutical industry, where sources of sample variability are accounted and the production process is fitted to include this variability to improve the final product quality. One example where this concept is well shown includes a study, 43 in which calibration laboratory samples (ie, NIR spectra) are produced with the same physical variability as the production samples in pharmaceutical analysis for the determination of the API concentration by using a similar granulation treatment to the one used in industry.…”

Section: State Of the Artmentioning

confidence: 99%

Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement

Georgouli

Osório

Rincón

et al. 2018

Journal of Chemometrics

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Generating more accurate, efficient, and robust classification models in chemometrics, able to address real‐world problems in food analysis, is intrinsically related with the amount of available calibration samples. In this paper, we propose a data augmentation solution to increase the performance of a classification model by generating realistic data augmented samples. The feasibility of this solution has been evaluated on 3 main different experiments where Fourier transform mid infrared (FT‐IR) spectroscopic data of vegetable oils were used for the identification of vegetable oil species in oil admixtures. Results demonstrate that data augmented samples improved the classification rate by around 19% in a single instrument validation and provided a significant 38% improvement in classification when testing in more than 10 different spectroscopic instruments to the calibration one.

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Section: State Of the Artmentioning

confidence: 99%

Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement

Georgouli

Osório

Rincón

et al. 2018

Journal of Chemometrics

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“…In this, scenario that demands consistency and efficiency in process and product line in the bakery industry, PAT, an emerging platform technology, was found as the exact solution. PAT is a robust framework for novel process manufacturing and quality assurance of raw materials, in-process material and end product, with the goal of ensuring high final product quality (Van den Berg, Lyndgaard, Sørensen, & Engelsen, 2013). In the 21st century, the USFDA's proposal to appreciate the adoption of high-quality technology to ensure good product manufacturing practices and product quality at pharmaceutical industry opened the way for PAT and USFDA's, process analytical technology was issued (Hitzmann et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Process analytical technology for bakery industry: A review

Jerome

Singh

Dwivedi

2019

J Food Process Engineering

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Bakery products are appreciably consumed by the populace of all age groups globally, owing to their flavor and easily digestible nature. The availability of diversified products like bread, cookies, biscuits, muffins, cakes, tortillas, rolls, wafers, and pies has further facilitated their consumption. However, variation in processing parameters and raw material quality triggers many biochemical changes, complex reactions, and physical modifications which are intricate and least understood in the food industry

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“…This would replace final product testing as quality is controlled during the production process, giving the possibility to "readjust" a process before the product is finished and thus minimizing rejects. 4 Over the last decades, infrared spectroscopy in combination with multivariate analysis has proven to be a powerful tool for food analyses and, specifically, for wine analyses. Partial least squares regression (PLSR) has been the most used calibration algorithm to predict chemical or physical parameters in wine from spectroscopic data.…”

Section: Introductionmentioning

confidence: 99%

Early detection of undesirable deviations in must fermentation using a portable FTIR‐ATR instrument and multivariate analysis

Cavaglia

Giussani

Mestres

et al. 2019

Journal of Chemometrics

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A portable FTIR-ATR spectrometer was used to monitor small-scale must fermentations (microvinifications) with the aims to describe the process and to early detect problematic fermentations. Twenty fermentations at normal operation conditions (NOC) and three fermentations that were intentionally deviated from NOC (yeast assimilable nitrogen deficiency-YAN) were monitored. FTIR-ATR spectra were registered after a minimum sample pretreatment during the fermentation process. In addition, density, sugars (glucose and fructose), and acetic acid contents were determined by traditional methods. Different multivariate analysis strategies (global and local models) were applied to the spectroscopic data to describe the evolution of the NOC fermentation and to early detect the abnormal fermentations. Global models based on principal component analysis (PCA) and partial least squaresdiscriminant analysis (PLS-DA) allowed to describe the evolution of fermentations in time and to correctly classify NOC and YAN fermentations. Abnormal deviations were successfully detected by developing one model for each sampling time. YAN experiments could be identified 49 hours after the beginning of the fermentations by means of Hotelling T 2 and residual F statistics. In conclusion, ATR-FTIR coupled to multivariate analysis showed great potential as a fast and simple at-line analysis tool to monitor wine fermentation and to early detect fermentation problems.

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Process Analytical Technology in the food industry

Cited by 95 publications

References 14 publications

Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement

Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement

Process analytical technology for bakery industry: A review

Early detection of undesirable deviations in must fermentation using a portable FTIR‐ATR instrument and multivariate analysis

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