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Advances in Application of Spectral Analysis in Dairy Products

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A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 41250

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Special Issue Editors

Prof. Dr. Ir. Ben Aernouts

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KU Leuven, Department of Biosystems, Division of Animal and Human Health Engineering, Livestock Technology research group, Campus Geel, Kleinhoefstraat 4, B-2440 Geel, Belgium
Interests: dairy farming; milk quality; near-infrared and mid-infrared spectroscopy; chemometrics; health and welfare monitoring; cattle management; precision livestock farming; sensors; biostatistics; time-series analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Ir. Clement Grelet

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Walloon Agricultural Research Center (CRA-W), Knowledge and valorization of agricultural products Department, Chaussée de Namur 24, B-5030 Gembloux, Belgium
Interests: Dairy farming; milk analysis; phenotyping; mid-infrared spectrometry; chemometrics; precision livestock farming; health, welfare and environmental impact monitoring; standardization of spectrometers

Dr. Ir. Ines Adriaens

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Guest Editor

KU Leuven, Department of Biosystems, Division of Animal and Human Health Engineering, Livestock Technology research group, Campus Geel, Kleinhoefstraat 4, B-2440 Geel, Belgium
Interests: precision livestock farming; robust computational tools; advanced data-processing and machine learning; interdisciplinary solutions; dairy and animal science

Special Issue Information

Dear Colleagues,

Worldwide, milk and derived dairy products are important components in the human diet as they are a balanced source of nutritious proteins, fat, carbohydrates, minerals, and vitamins. Because the produced milk originates from millions of dairy farms, being collected, processed and distributed again by several thousand dairy processors and retailers, rapid and cost-effective quality control is essential at all levels of the dairy production chain. Already for decades, high-throughput spectroscopic techniques are the basis for dairy quality control in the laboratory or at the processing plant, although continuously evolving, improving and enlightening new aspects. Apart from the perspective of food technology and the economic value of the dairy product, milk composition and quality also reveal important information on the genetic potential, performance, health and welfare of the animals that produce the milk. As such, the same analytical techniques are being used for precision phenotyping, health and welfare monitoring, and optimization of inputs and outputs. Upon the technology becoming more accurate, robust, and flexible, new applications of spectral analysis of dairy products are important topics of today’s research and innovation.

This Special Issue deals with novel advances in the broad field of spectroscopic dairy analysis, from farm to fork, covering all aspects of dairy quality and animal monitoring and improvement. It involves spectroscopic studies of nutritional, sensory, sanitary, and technological properties of milk and derived products, and includes methods for authentication of products and adulteration detection. The genetic evaluation and direct or indirect monitoring of health, welfare, performance and efficiency of animals based on the spectroscopic milk analysis can be further addressed in this Special Issue. Next to new advances made in the laboratory, novel strategies for spectroscopic dairy analysis at the farm or the food processing plant, as well as inventive chemometrics, multi-variate and statistical data analysis approaches, are strongly welcomed. In this Special Issue, we aim to publish original research results and review papers.

Prof. Dr. Ir. Ben Aernouts
Dr. Ir. Clément Grelet
Dr. Ir. Ines Adriaens
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Milk and dairy products
Spectroscopic analysis
Near and mid-infrared
Dairy nutritional, sanitary, sensory and technological quality properties
Authentication and fraud and adulteration detection
Milk metabolomics
Animal health and welfare monitoring
Precision livestock farming and phenotyping
Big-data, chemometrics and biostatistical tools
On-line analysis

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Published Papers (10 papers)

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Research

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13 pages, 1872 KiB

Open AccessArticle

Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits

by Maria Frizzarin, Isobel Claire Gormley, Alessandro Casa and Sinéad McParland

Foods 2021, 10(12), 3084; https://doi.org/10.3390/foods10123084 - 11 Dec 2021

Viewed by 2388

Abstract

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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16 pages, 6264 KiB

Open AccessArticle

Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition

by Sanna Uusitalo, José Diaz-Olivares, Juha Sumen, Eero Hietala, Ines Adriaens, Wouter Saeys, Mikko Utriainen, Lilli Frondelius, Matti Pastell and Ben Aernouts

Foods 2021, 10(11), 2686; https://doi.org/10.3390/foods10112686 - 3 Nov 2021

Cited by 17 | Viewed by 3132

Abstract

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry–Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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16 pages, 912 KiB

Open AccessArticle

Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

by Octave S. Christophe, Clément Grelet, Carlo Bertozzi, Didier Veselko, Christophe Lecomte, Peter Höeckels, Andreas Werner, Franz-Josef Auer, Nicolas Gengler, Frédéric Dehareng and Hélène Soyeurt

Foods 2021, 10(9), 2235; https://doi.org/10.3390/foods10092235 - 21 Sep 2021

Cited by 11 | Viewed by 3044

Abstract

Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals’ variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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18 pages, 3419 KiB

Open AccessArticle

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow’s Lactation

by Amira Rachah, Olav Reksen, Valeria Tafintseva, Felicia Judith Marie Stehr, Elling-Olav Rukke, Egil Prestløkken, Adam Martin, Achim Kohler and Nils Kristian Afseth

Foods 2021, 10(9), 2033; https://doi.org/10.3390/foods10092033 - 29 Aug 2021

Cited by 5 | Viewed by 3720

Abstract

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows’ lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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13 pages, 1933 KiB

Open AccessArticle

Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms

by Lukas Spieß, Peter de Peinder and Harrie van den Bijgaart

Foods 2021, 10(5), 1111; https://doi.org/10.3390/foods10051111 - 18 May 2021

Cited by 2 | Viewed by 2616

Abstract

Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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14 pages, 617 KiB

Open AccessArticle

Fatty Acid Prediction in Bovine Milk by Attenuated Total Reflection Infrared Spectroscopy after Solvent-Free Lipid Separation

by Christopher Karim Akhgar, Vanessa Nürnberger, Marlene Nadvornik, Margit Velik, Andreas Schwaighofer, Erwin Rosenberg and Bernhard Lendl

Foods 2021, 10(5), 1054; https://doi.org/10.3390/foods10051054 - 11 May 2021

Cited by 7 | Viewed by 3082

Abstract

In the present study, a novel approach for mid-infrared (IR)-based prediction of bovine milk fatty acid composition is introduced. A rapid, solvent-free, two-step centrifugation method was applied in order to obtain representative milk fat fractions. IR spectra of pure milk lipids were recorded with attenuated total reflection Fourier-transform infrared (ATR-FT-IR) spectroscopy. Comparison to the IR transmission spectra of whole milk revealed a higher amount of significant spectral information for fatty acid analysis. Partial least squares (PLS) regression models were calculated to relate the IR spectra to gas chromatography/mass spectrometry (GC/MS) reference values, providing particularly good predictions for fatty acid sum parameters as well as for the following individual fatty acids: C10:0 (R²_P = 0.99), C12:0 (R²_P = 0.97), C14:0 (R²_P = 0.88), C16:0 (R²_P = 0.81), C18:0 (R²_P = 0.93), and C18:1cis (R²_P = 0.95). The IR wavenumber ranges for the individual regression models were optimized and validated by calculation of the PLS selectivity ratio. Based on a set of 45 milk samples, the obtained PLS figures of merit are significantly better than those reported in literature using whole milk transmission spectra and larger datasets. In this context, direct IR measurement of the milk fat fraction inherently eliminates covariation structures between fatty acids and total fat content, which poses a common problem in IR-based milk fat profiling. The combination of solvent-free lipid separation and ATR-FT-IR spectroscopy represents a novel approach for fast fatty acid prediction, with the potential for high-throughput application in routine lab operation. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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18 pages, 8993 KiB

Open AccessArticle

Whey Protein Powder Analysis by Mid-Infrared Spectroscopy

by Rose Saxton and Owen M. McDougal

Foods 2021, 10(5), 1033; https://doi.org/10.3390/foods10051033 - 10 May 2021

Cited by 21 | Viewed by 7969

Abstract

There is an ever-expanding number of high protein dietary supplements marketed as beneficial to athletes, body builders, infant formulas, elder care, and animal feed. Consumers will pay more for products with high protein per serving data on their nutritional labels, making the accurate reporting of protein content critical to customer confidence. The Kjeldahl method (KM) is the industry standard to quantitate dairy proteins, but the result is based on nitrogen content, which is an approximation of nitrogen attributable to protein in milk. Product tampering by third-party manufacturers is an issue, due to the lack of United States Food and Drug Administration regulation of nutraceutical products, permitting formulators to add low-cost nitrogen-containing components to artificially inflate the KM approximated protein content in products. Optical spectroscopy is commonly used for quality control measurements and has been identified as having the potential to complement the KM as a more nuanced testing measure of dairy protein. Mid-infrared (MIR) spectroscopy spectra of eight protein standards provided qualitative characterization of each protein by amide I and amide II peak absorbance wavenumber. Protein doping experiments revealed that as protein amounts were increased, the amide I/II peak shape changed from the broad protein powder peaks to the narrower peaks characteristic of the individual protein. Amino acid doping experiments with lysine, glutamic acid, and glycine, determined the limit of detection by MIR spectroscopy as 25%, suggesting that MIR spectroscopy can provide product quality assurance complementary to dairy protein measurement by the KM. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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15 pages, 3325 KiB

Open AccessArticle

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy

by Yisen Liu, Songbin Zhou, Wei Han, Chang Li, Weixin Liu, Zefan Qiu and Hong Chen

Foods 2021, 10(4), 785; https://doi.org/10.3390/foods10040785 - 6 Apr 2021

Cited by 20 | Viewed by 3438

Abstract

Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R²) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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17 pages, 732 KiB

Open AccessArticle

Fourier Transform Infrared Spectroscopy as a Tool to Study Milk Composition Changes in Dairy Cows Attributed to Housing Modifications to Improve Animal Welfare

by Mazen Bahadi, Ashraf A. Ismail and Elsa Vasseur

Foods 2021, 10(2), 450; https://doi.org/10.3390/foods10020450 - 18 Feb 2021

Cited by 8 | Viewed by 3808

Abstract

Animal welfare status is assessed today through visual evaluations requiring an on-farm visit. A convenient alternative would be to detect cow welfare status directly in milk samples, already routinely collected for milk recording. The objective of this study was to propose a novel approach to demonstrate that Fourier transform infrared (FTIR) spectroscopy can detect changes in milk composition related to cows subjected to movement restriction at the tie stall with four tie-rail configurations varying in height and position (TR1, TR2, TR3 and TR4). Milk mid-infrared spectra were collected on weekly basis. Long-term average spectra were calculated for each cow using spectra collected in weeks 8–10 of treatment. Principal component analysis was applied to spectral averages and the scores of principal components (PCs) were tested for treatment effect by mixed modelling. PC7 revealed a significant treatment effect (p = 0.01), particularly for TR3 (configuration with restricted movement) vs. TR1 (recommended configuration) (p = 0.03). The loading spectrum of PC7 revealed high loadings at wavenumbers that could be assigned to biomarkers related to negative energy balance, such as β-hydroxybutyrate, citrate and acetone. This observation suggests that TR3 might have been restrictive for cows to access feed. Milk FTIR spectroscopy showed promising results in detecting welfare status and housing conditions in dairy cows. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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Review

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23 pages, 1703 KiB

Open AccessReview

Recent Advances in Portable and Handheld NIR Spectrometers and Applications in Milk, Cheese and Dairy Powders

by Yuanyuan Pu, Dolores Pérez-Marín, Norah O’Shea and Ana Garrido-Varo

Foods 2021, 10(10), 2377; https://doi.org/10.3390/foods10102377 - 8 Oct 2021

Cited by 47 | Viewed by 6186

Abstract

Quality and safety monitoring in the dairy industry is required to ensure products meet a high-standard based on legislation and customer requirements. The need for non-destructive, low-cost and user-friendly process analytical technologies, targeted at operators (as the end-users) for routine product inspections is increasing. In recent years, the development and advances in sensing technologies have led to miniaturisation of near infrared (NIR) spectrometers to a new era. The new generation of miniaturised NIR analysers are designed as compact, small and lightweight devices with a low cost, providing a strong capability for on-site or on-farm product measurements. Applying portable and handheld NIR spectrometers in the dairy sector is increasing; however, little information is currently available on these applications and instrument performance. As a result, this review focuses on recent developments of handheld and portable NIR devices and its latest applications in the field of dairy, including chemical composition, on-site quality detection, and safety assurance (i.e., adulteration) in milk, cheese and dairy powders. Comparison of model performance between handheld and bench-top NIR spectrometers is also given. Lastly, challenges of current handheld/portable devices and future trends on implementing these devices in the dairy sector is discussed. Full article

(This article belongs to the Special Issue Advances in Application of Spectral Analysis in Dairy Products)

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