Special Issue "Development of Process Analytical Technologies (PAT) for Dairy and Infant Formula Manufacture"

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

Deadline for manuscript submissions: 20 January 2021.

Special Issue Editor

Dr. Maria Markiewicz-Keszycka
Website
Guest Editor
UCD School of Agriculture and Food Science, UCD, Belfield, Dublin 4, Ireland
Interests: Animal Nutrition; Dairy Science; Meat Quality; Dairy; Animal Production; Animal Science; Spectroscopy; Process Analytical Technology

Special Issue Information

The new paradigm in the food industry is Quality by Design (QbD), inspired by the Process Analytical Technology (PAT) concept introduced originally for pharmaceutical manufacture.  The hypothesis behind this paradigm shift is that the quality of the (food) products can and should be incorporated by process design and not by post-production quality testing. PAT is a framework for innovative process manufacturing and quality assurance through timely measurements (i.e., in-line monitoring) of critical quality attributes. The potential impact of PAT on product quality and safety, process efficiency and yields is tremendous. Smart sensing solutions can not only provide real-time quality assurance but also provide “big data” which facilitates increased product traceability as product batches can be referenced to their processing history. 

For this Special Issue, I would like to encourage you to submit your research related to rapid, effective, and transparent systems which provide data on the quality and composition of milk, milk powders, and dairy products. I invite you to submit manuscripts on rapid methods that facilitate fast (eventually on-line) collection of product-specific information, effective in order to achieve real control over raw material and end-product quality and safety, and transparent in order to develop confidence in their utility by prospective customers. It is unlikely that any analytical instrument is capable of fully characterising the quality and safety of a given food product, thus data from a number of sensors combined to deliver information required to achieve robust on-line monitoring are also welcome. The aim of this Special Issue is to present an overview of the latest applications of feasible PAT tools for both the dairy industry and the academic community. 

Dr. Maria Markiewicz-Keszycka
Guest Editor

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 papers will be 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 monthly 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 2000 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

  • Process Analytical Technology 
  • Dairy products 
  • Milk powders 
  • Infant formula 
  • Spectroscopy 
  • Chemometrics 
  • Fingerprinting 
  • Authentication

Published Papers (4 papers)

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Research

Open AccessArticle
Electronic Nose for Monitoring Odor Changes of Lactobacillus Species during Milk Fermentation and Rapid Selection of Probiotic Candidates
Foods 2020, 9(11), 1539; https://doi.org/10.3390/foods9111539 - 26 Oct 2020
Abstract
Probiotic bacteria have been associated with a unique production of aroma compounds in fermented foods but rapid methods for discriminating between foods containing probiotic, moderately probiotic, or non-probiotic bacteria remain aloof. An electronic nose (e-nose) is a high-sensitivity instrument capable of non-invasive volatile [...] Read more.
Probiotic bacteria have been associated with a unique production of aroma compounds in fermented foods but rapid methods for discriminating between foods containing probiotic, moderately probiotic, or non-probiotic bacteria remain aloof. An electronic nose (e-nose) is a high-sensitivity instrument capable of non-invasive volatile measurements of foods. In our study, we applied the e-nose to differentiate probiotic, moderately probiotic, and non-probiotic Lactobacillus bacteria strains at different fermentation time points (0th, 4th, and 11th) of milk fermentation. The pH of the changing milk medium was monitored with their corresponding increase in microbial cell counts. An e-nose with two gas chromatographic columns was used to develop classification models for the different bacteria groups and time points and to monitor the formation of the aromatic compounds during the fermentation process. Results of the e-nose showed good classification accuracy of the different bacteria groups at the 0th (74.44% for column 1 and 82.78% for column 2), the 4th (89.44% for column 1 and 92.22% for column 2), and the 11th (81.67% for column 1 and 81.67% for column 2) hour of fermentation. The loading vectors of the classification models showed the importance of some specific aroma compounds formed during the fermentation. Results show that aroma monitoring of the fermentation process with the e-nose is a promising and reliable analytical method for the rapid classification of bacteria strains according to their probiotic activity and for the monitoring of aroma changes during the fermentation process. Full article
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Open AccessArticle
Monitoring Viscosity and Total Solids Content of Milk Protein Concentrate Using an Inline Acoustic Flowmeter at Laboratory Scale
Foods 2020, 9(9), 1310; https://doi.org/10.3390/foods9091310 - 17 Sep 2020
Abstract
Control of milk concentrate viscosity and total solids (TS) content prior to spray drying can improve dairy ingredient manufacture. However, the availability of hygienic and appropriately pressure rated process viscometers for inline monitoring of viscosity is limited. An acoustic flowmeter (FLOWave) is an [...] Read more.
Control of milk concentrate viscosity and total solids (TS) content prior to spray drying can improve dairy ingredient manufacture. However, the availability of hygienic and appropriately pressure rated process viscometers for inline monitoring of viscosity is limited. An acoustic flowmeter (FLOWave) is an inline process analytical technology (PAT) tool that measures changes in acoustic signals in response to changes in liquid properties (i.e., acoustic transmission (AT), acoustic impedance (AI), temperature and volume flowrate). In this study, an acoustic flowmeter is evaluated as an inline PAT tool for monitoring viscosity of milk protein concentrate (MPC85), protein and TS content of (MPC85), and standardised MPC (sMPC). Laboratory scale experiments were carried out at 45 °C for five different concentrations (4–21%) of MPC85 and sMPC. Results showed that AT decreased with an increase in MPC85 viscosity (e.g., AT was 98.79 ± 0.04% and 86.65 ± 0.17% for 4% and 21% TS content, respectively). Non-linear regression was carried out to develop a relationship between AT and offline viscosity (R2 (coefficient of determination) value = 0.97 and standard error of prediction = 1.86 mPa·s). AI was observed to increase at higher protein and TS content which was dependent on protein to total solid ratio (P_TSR). Multiple linear regression was carried out to develop the relationship between AI, protein content, TS content and P_TSR. Results demonstrated that AI could be used to monitor the protein and TS content of milk protein concentrate (R2 > 0.96). Overall this study demonstrated the potential of an inline acoustic flowmeter for monitoring process viscosity, protein and TS during dairy concentrate processing. Full article
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Open AccessArticle
Effects of Morphology on the Bulk Density of Instant Whole Milk Powder
Foods 2020, 9(8), 1024; https://doi.org/10.3390/foods9081024 - 31 Jul 2020
Cited by 2
Abstract
The chemical and physical properties of instant whole milk powder (IWMP), such as morphology, protein content, and particle size, can affect its functionality and performance. Bulk density, which directly determines the packing cost and transportation cost of milk powder, is one of the [...] Read more.
The chemical and physical properties of instant whole milk powder (IWMP), such as morphology, protein content, and particle size, can affect its functionality and performance. Bulk density, which directly determines the packing cost and transportation cost of milk powder, is one of the most important functional properties of IWMP, and it is mainly affected by physical properties, e.g., morphology and particle size. This work quantified the relationship between morphology and bulk density of IWMP and developed a predictive model of bulk density for IWMP. To obtain milk powder samples with different particle size fractions, IWMP samples of four different brands were sieved into three different particle size range groups, before using the simplex-centroid design (SCD) method to remix the milk powder samples. The bulk densities of these remixed milk powder samples were then measured by tap testing, and the particles’ shape factors were extracted by light microscopy and image processing. The number of variables was decreased by principal component analysis and partial least squares models and artificial neural network models were built to predict the bulk density of IWMP. It was found that different brands of IWMP have different morphology, and the bulk density trends versus the shape factor changes were similar for the different particle size range groups. Finally, prediction models for bulk density were developed by using the shape factors and particle size range fractions of the IWMP samples. The good results of these models proved that predicting the bulk density of IWMP by using shape factors and particle size range fractions is achievable and could be used as a model for online model-based process monitoring. Full article
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Open AccessArticle
Investigation of Raman Spectroscopy (with Fiber Optic Probe) and Chemometric Data Analysis for the Determination of Mineral Content in Aqueous Infant Formula
Foods 2020, 9(8), 968; https://doi.org/10.3390/foods9080968 - 22 Jul 2020
Abstract
This study investigated the use of Raman spectroscopy (RS) and chemometrics for the determination of eight mineral elements (i.e., Ca, Mg, K, Na, Cu, Mn, Fe, and Zn) in aqueous infant formula (INF). The samples were prepared using infant formula powder reconstituted to [...] Read more.
This study investigated the use of Raman spectroscopy (RS) and chemometrics for the determination of eight mineral elements (i.e., Ca, Mg, K, Na, Cu, Mn, Fe, and Zn) in aqueous infant formula (INF). The samples were prepared using infant formula powder reconstituted to concentrations of 3%–13% w/w (powder: water) (n = 83). Raman spectral data acquisition was carried out using a non-contact fiber optic probe on the surface of aqueous samples in 50–3398 cm−1. ICP-AES was used as a reference method for the determination of the mineral contents in aqueous INF samples. Results showed that the best performing partial least squares regression (PLSR) models developed for the prediction of minerals using all samples for calibration achieved R2CV values of 0.51–0.95 with RMSECVs of 0.13–2.96 ppm. The PLSR models developed and validated using separate calibration (n = 42) and validation (n = 41) samples achieved R2CVs of 0.93, 0.94, 0.91, 0.90, 0.97, and 0.94, R2Ps of 0.75, 0.77, 0.31, 0.60, 0.84, and 0.80 with RMSEPs of 3.17, 0.29, 3.45, 1.51, 0.30, and 0.25 ppm for the prediction of Ca, Mg, K, Na, Fe, and Zn respectively. This study demonstrated that RS equipped with a non-contact fiber optic probe and combined with chemometrics has the potential for timely quantification of the mineral content of aqueous INF during manufacturing. Full article
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