Academic literature on the topic 'Chemometrics: Dairy Industry'

Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

The process of cheese-making has long been part of human food culture and nowadays dairy represents a large sector of the food industry. Being the main byproduct of cheese-making, the revalorization of milk whey is nowadays one of the primary goals in alignment with the principles of the circular economy. In the present paper, a deep and detailed investigation of short endogenous peptides in milk and its byproducts (whole whey, skimmed whey, and whey permeate) was carried out by high-resolution mass spectrometry, with a dedicated suspect screening data acquisition and data analysis approach. A total of 79 short peptides was tentatively identified, including several sequences already known for their exerted biological activities. An unsupervised chemometric approach was then employed for highlighting the differences in the short peptide content among the four sets of samples. Whole and skimmed whey showed not merely a higher content of short bioactive peptides compared to whole milk, but also a peculiar composition of peptides that are likely generated during the process of cheese-making. The results clearly demonstrate that whey represents a valuable source of bioactive compounds and that the set-up of processes of revalorization of milk byproducts is a promising path in the obtention of high revenue-generating products from dairy industrial waste.

Abstract Herbals and herbal preparations are complex mixtures with numerous natural compounds in an uncharacterized matrixtruly multicomponent systems. This is in contrast to most of the samples of the pharmaceutical and, in part, the food industry, where primarily single compounds have to be analyzed. Recently, models for the characterization of multicomponent systems with near-IR, NMR, and MS combined with chemometric tools have been developed. However, the complexity and sophistication of such methods still prevent their general applicability to the QC of herbals. On the other hand, modern TLC is a well-established method with a long tradition. The typical chromatograms visualize even complex multicomponent systems in a special manner. The technique is rapid, comparatively simple, robust, and extremely versatile. HPTLC can not only confirm but also establish identity. It is also an ideal screening tool for adulterations and is highly suitable for evaluation and monitoring of cultivation, harvesting, and extraction processes and testing of stability. To substantiate these claims, several examples taken from daily work are provided and discussed in this paper.

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.

Abstract The oxidative action of chemical substances present in dairy products may contribute to the darkening of the product. Product color is one of the first factors to be considered by the consumer for acceptance or rejection. In the food industry, the color parameter is measured using colorimeters and spectrophotometers; nevertheless, the use of digital images for colorimetric tests has been surveyed in the food area. Therefore, the present work aimed at investigating for 45 days the chemical, physicochemical and colorimetric alterations of creamy dairy dessert with white chocolate flavor and strawberry-flavored yogurt. These alterations were monitored by the analysis of the parameters pH, acidity, soluble solids content, in addition to spectroscopy in the middle-infrared region and digital images. The data collected were processed in a computational environment applying chemometric tools. As result, it was verified that there were alterations in the parameters evaluated; nonetheless, the acidity of the dairy dessert remained constant during the storage period. From the Principal Component Analysis (PCA) using the color variables, it was observed that the samples were grouped and separated by type and storage time in agreement with the visually observed colorimetric modifications.

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

AbstractThe nutrition of grazing ruminants can be optimized by allocating pasture according to its nutritive characteristics, provided that nutritive concentrations are determined in near-real time. Current proximal spectrometers can provide accurate predictive results but are bulky and expensive. This study compared an industry standard, ‘control’, proximal spectrometer, often used for scientific estimation of pasture nutrient concentrations in situ (350–2500 nm spectral range), with three lower-cost, ‘next-generation’, handheld spectrometers. The candidate sensors included a hyperspectral camera (397–1004 nm), and two handheld spectrometers (908–1676 nm and 1345–2555 nm respectively). Pasture samples (n = 145) collected from two paddocks on a working Australian dairy farm, over three timepoints, were scanned in situ by each instrument and then analysed for eight nutritive parameters. Chemometric models were then developed for each nutrient using data from each sensor (split into 80:20 calibration and validation sets). According to Lin’s Concordance Correlation Coefficient (LCCC) from independent validation (n = 29), the hyperspectral camera was the best candidate instrument (LCCC from 0.31 to 0.85, and 0.67 on average), rivalling the control sensor (LCCC from 0.41 to 0.84, and 0.67 on average). Consideration was given to whether the hyperspectral camera’s success was due to spectral range or data type/capture method. It was found that the 400–920 nm (trimmed) spectral region was slightly less sensitive in principle to nutrient concentrations than higher spectral ranges. Therefore, the predictive performance of the camera was attributed to the advantage of gathering data as hyperspectral images as opposed to single spectra.

Questo lavoro di tesi risponde al bisogno dell’industria lattiero-casearia di incrementare la produttività e allo stesso tempo soddisfare la richiesta dei consumatori di prodotti di elevata qualità. A tal fine, si possono proporre alle aziende lattiero-casearie nuovi metodi per migliorare la comprensione e il monitoraggio dei processi di produzione. La Process Analytical Technology (PAT) rappresenta uno strumento ideale per raggiungere questo scopo, grazie a sensori in grado di eseguire analisi rapide, green, non distruttive ed in tempo reale. Le tecniche più utilizzate in questo campo sono la spettroscopia del vicino e del medio infrarosso (NIR e MIR, rispettivamente), che forniscono informazioni chimico-fisiche sul prodotto grazie a sonde installate direttamente in punti critici del processo. Tuttavia, queste tecniche hanno lo svantaggio di fornire risultati (spettri) difficilmente interpretabili senza l’aiuto di un adeguato metodo statistico. In questo contesto, gli algoritmi Chemiometrici permettono l’estrazione di informazioni rilevanti dai dati spettroscopici, permettendo la comprensione del sistema studiato. La prima parte del presente lavoro è focalizzata sul monitoraggio del processo di coagulazione, uno dei momenti più critici della caseificazione. A tal fine, è stata utilizzata una sonda FT-NIR per acquisire spettri durante il processo di coagulazione. variando alcuni fattori tecnologici cruciali, come temperatura, contenuto di grasso e pH, secondo un disegno Box-Behnken. Attraverso l’algoritmo Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) è stato possibile ottenere sia una efficiente descrizione delle tre differenti fasi del processo di coagulazione, sia lo sviluppo di carte di controllo multivariate (Multivariate Statistical Process Control charts, MSPC), capaci di individuare possibili non-conformità fin dai primi momenti del processo. Inoltre, il metodo ANOVA-Simultaneous Component Analysis (ASCA) è stato applicato ai dati spettrali al fine di ottenere una migliore comprensione della coagulazione, evidenziando in che modo ogni fattore sperimentale influenzi il processo. Nella seconda parte del lavoro, la spettroscopia FT-NIR è stata studiata come possibile strumento per sostituire le tecniche standard, come il Formagraph, per valutare l’attitudine alla coagulazione. Le prove di coagulazione sono state effettuate usando differenti campioni di latte in polvere. L’utilizzo dell’algoritmo MCR-ALS ha permesso la valutazione della miglior polvere in termini di attitudine alla coagulazione e, inoltre, ha evidenziato la non significatività degli effetti della concentrazione di CaCl2 e del trattamento termico del latte ricostituito sul tempo di coagulazione. Infine, le prove sperimentali eseguite con miscele di latte scremato e percentuali più elevate di latte ricostituito hanno mostrato una coagulazione più lenta. L’ultima parte del lavoro ha riguardato l’utilizzo della spettroscopia MIR per monitorare la produzione di galattooligosaccaridi (GOS) dal siero di formaggio, allo scopo di valorizzare questo prodotto e di ottimizzare il processo. La regressione Partial Least Square (PLS) è stata utilizzata con l’obiettivo di predire le componenti specifiche derivanti dalle differenti reazioni enzimatiche studiate. In conclusione, l’applicazione dei metodi proposti permetterà un efficiente controllo del processo garantendo un modesto impatto ambientale e soddisfacendo allo stesso tempo requisiti di legge e esigenze dei consumatori. Infine, l’affidabilità degli approcci PAT può essere rafforzata da future applicazioni industriali.
This thesis work wants to answer the need of dairy industry to increase productivity while satisfying the consumers request for higher quality products. In order to do that, dairy companies need innovative methods to improve the understanding and the monitoring of production processes. Process Analytical Technology (PAT) approaches are the perfect tool for this purpose, as they use green, fast, non-invasive and non-destructive sensors that allow to perform measurements in real time. The most used techniques in this field are Near- and Mid-Infrared (NIR and MIR, respectively) spectroscopy, whose probes can be directly installed in critical points of the process providing both physical and chemical information of the product. However, these techniques have the drawback of providing results (spectra) difficult to be interpreted without proper statistical tools. In this context, Chemometric methods and algorithms allow the extraction of relevant information from spectroscopic data, providing a better understanding of the studied system. The first part of the present work focused on the monitoring of the coagulation process, one of the most critical moments of cheesemaking. To this aim, an FT-NIR spectroscopy system was used, acquiring spectra along the rennet coagulation process. According to a Box-Behnken experimental design, several coagulation trials were carried out, changing crucial technological factors, such as temperature, fat content and pH. Through Multivariate Curve Resolution optimized by Alternating Least Squares (MCR-ALS) algorithm it was possible to both have a reliable description of the three different coagulation phases and, most importantly, to build Multivariate Statistical Process Control (MSPC) charts, able to detect failures from the first moment of the process. Moreover, ANOVA-Simultaneous Component Analysis (ASCA) method was applied on spectral data to obtain a better understanding of the process, highlighting in which way each physicochemical parameter affects the process. In the second part of the work, FT-NIR spectroscopy was tested as a possible tool to replace the golden standards of coagulation ability, i.e. Formagraph. Coagulation trials were carried out using different milk powder samples. The use of MCR-ALS algorithm permitted the assessment of the best powder in terms of coagulation attitude and, in addition, it highlighted the non-significant effect on coagulation occurrence of CaCl2 concentration and of heat treatment on reconstituted milk. Finally, experimental trials carried out with mixtures of skimmed milk and reconstituted milk showed a slower coagulation time when a higher reconstituted milk percentage was used. The last part of the work regarded the use of MIR spectroscopy to monitor Galactooligosaccharides (GOS) production from cheese whey, in order to avoid the waste of this compound and to optimize the studied process. To do so, Partial Least Square (PLS) regression was used to predict the specific compounds resultant from the different enzymatic reaction studied. In conclusion, the application of the proposed methods will implicate, with a modest environmental impact, an efficient control of the process, satisfying at the same time law requirements and consumers’ needs. Furthermore, reliability of PAT approaches could be strengthened by future industrial applications.