Chemometric authentication of farming systems of origin of food (milk and ripened cheese) using infrared spectra, fatty acid profiles, flavor fingerprints, and sensory descriptions

Leveraging milk mid-infrared spectroscopy to authenticate animal welfare, farming practices, and dairy systems of Parmigiano Reggiano cheese

Increasing consumer concerns underscore the importance of verifying the practices and origins of food, especially certified premium products. The aim of this study was to evaluate the ability of Fourier-transform mid-infrared (FT-MIR) spectroscopy to authenticate animal welfare parameters, farming practices, and dairy systems. Data on farm characteristics were obtained from the Parmigiano Reggiano Consortium in northern Italy. Animal welfare data were collected by trained veterinarians using the assessment protocol developed by the Italian National Reference Center for Animal Welfare (CReNBA), and bulk milk test-day data were obtained from the laboratory of the Breeders Association of the Emilia Romagna Region. A merged final dataset of 12,083 bulk FT-MIR spectra records from 949 farms was created. Using a nonhierarchical clustering approach, the farms were classified into 5 dairy systems: 2 traditional systems comprising farms located in either the Apennines or the Po Plain; 2 modern systems, one that used TMR and one did not; and one traditional dairy system comprising farms rearing local breeds. To evaluate the ability of bulk milk to capture differences in farming systems, we conducted an ANOVA on milk composition. The linear models included the following effects: season, dairy system, farm, and the interaction between dairy system and season. The effect of the dairy system was significant for all milk composition traits. A 10-iteration linear discriminant analysis was used to evaluate the discriminative ability of the spectra in classifying farming practices and dairy systems. The average results of the area under the receiver operating characteristic curve revealed good authentication performance for genetic type (0.98), housing system (0.91), and feeding system (0.89), and medium-low authentication performance for geographical area (0.70); poor results were obtained for the percentage of concentrate in the diet and animal welfare parameters (0.57-0.64). With regard to dairy systems, the best result was obtained when dairy systems were grouped into 2 simplified categories, traditional versus modern (0.89), instead of the 5 categories (0.87). The results of this study show that FT-MIR is a useful tool for authenticating farming practices and dairy systems, but not animal welfare as defined by CReNBA evaluation criteria. Our results show that infrared spectroscopy has the potential to authenticate dairy products and quality label certifications.

Utility of dairy microbiome as a tool for authentication and traceability

Milk microbiome contributes substantially to the formation of specific organoleptic and physicochemical characteristics of dairy products. The assessment of the composition and abundance of milk microbiota is a challenging task strongly influenced by many environmental factors. Specific dairy products may be designated by the Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) labeling, which however, occasionally fail to differentiate them according to specific quality characteristics, which are defined by different microbiota-driven reactions. Combining the above limitations, the scope of the present study, was to summarize the existing information toward three main issues. First, to assess the influence level of the diet type and grazing to rumen-GI tract, mammary gland, and udder microbiome formation in ruminants. Second, to discuss the factors affecting milk microbiota, as well as the effect of the endo-mammary route on milk microbial taxa. Lastly, to evaluate "milk microbiome" as a tool for product differentiation, according to origin, which will contribute to a more robust PDO and PGI labeling. Although the limitations are still a matter of fact (especially considering the sample collection, process, evaluation, and avoidance of its contamination), significant progress has been made, regarding the identification of the factors affecting dairy products' microbiota and its core composition. In conclusion, although so far not totally efficient in dairy products molecular identification, with the progress in soil, water, plant, and animal host's microbiota assembly's characterization, microbiomics could provide a powerful tool for authentication and traceability of dairy products.

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Discriminative power of DNA-based, volatilome, near infrared spectroscopy, elements and stable isotopes methods for the origin authentication of typical Italian mountain cheese using sPLS-DA modeling

Origin authentication methods are pivotal in counteracting frauds and provide evidence for certification systems. For these reasons, geographical origin authentication methods are used to ensure product origin. This study focused on the origin authentication (i.e. at the producer level) of a typical mountain cheese origin using various approaches, including shotgun metagenomics, volatilome, near infrared spectroscopy, stable isotopes, and elemental analyses. DNA-based analysis revealed that viral communities achieved a higher classification accuracy rate (97.4 ± 2.6 %) than bacterial communities (96.1 ± 4.0 %). Non-starter lactic acid bacteria and phages specific to each origin were identified. Volatile organic compounds exhibited potential clusters according to cheese origin, with a classification accuracy rate of 90.0 ± 11.1 %. Near-infrared spectroscopy showed lower discriminative power for cheese authentication, yielding only a 76.0 ± 31.6 % classification accuracy rate. Model performances were influenced by specific regions of the infrared spectrum, possibly associated with fat content, lipid profile and protein characteristics. Furthermore, we analyzed the elemental composition of mountain Caciotta cheese and identified significant differences in elements related to dairy equipment, macronutrients, and rare earth elements among different origins. The combination of elements and isotopes showed a decrease in authentication performance (97.0 ± 3.1 %) compared to the original element models, which were found to achieve the best classification accuracy rate (99.0 ± 0.01 %). Overall, our findings emphasize the potential of multi-omics techniques in cheese origin authentication and highlight the complexity of factors influencing cheese composition and hence typicity.

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

Preliminary Feasibility of Near-Infrared Spectroscopy to Authenticate Grazing in Dairy Goats through Milk and Faeces Analysis

Consumers are increasingly prone to request information about the production systems of the food they buy. For this purpose, certification and authentication methodologies are necessary not only to protect the choices of consumers, but also to protect producers and production systems. The objective of this preliminary work was to authenticate the grazing system of dairy goats using Near-Infrared Spectroscopy (NIRS) analyses of milk and faeces of the animals. Spectral information and several mathematical pre-treatments were used for the development of six discriminant models based on different algorithms for milk and faeces samples. Results showed that the NIRS spectra of both types of samples had some differences when the two feeding regimes were compared. Therefore, good discrimination rates were obtained with both strategies (faeces and milk samples), with classification percentages of up to 100% effectiveness. Discrimination of feeding regime and grazing authentication based on NIRS analysis of milk samples and an alternative sample such as faeces is considered as a potential approach for dairy goats and small ruminant production.

The Effect of the Feeding System on Fat Deposition in Yak Subcutaneous Fat

Fat deposition is very important to the growth and reproduction of yaks. In this study, the effect of the feeding system on fat deposition in yaks was explored by transcriptomics and lipidomics. The thickness of the subcutaneous fat in yaks under stall (SF) and graze feeding (GF) was evaluated. The transcriptomes and lipidomes of the subcutaneous fat in yaks under different feeding systems were detected by RNA-sequencing (RNA-Seq) and non-targeted lipidomics based on ultrahigh-phase liquid chromatography tandem mass spectrometry (UHPLC-MS), respectively. The differences in lipid metabolism were explored, and the function of differentially expressed genes (DEGs) was evaluated by gene ontology (GO) and Kyoto encyclopedia of genes and genome (KEGG) analysis. Compared with GF yaks, SF yaks possessed stronger fat deposition capacity. The abundance of 12 triglycerides (TGs), 3 phosphatidylethanolamines (PEs), 3 diglycerides (DGs), 2 sphingomyelins (SMs) and 1 phosphatidylcholine (PC) in the subcutaneous fat of SF and GF yaks was significantly different. Under the mediation of the cGMP-PKG signaling pathway, the blood volume of SF and GF yaks may be different, which resulted in the different concentrations of precursors for fat deposition, including non-esterified fatty acid (NEFA), glucose (GLU), TG and cholesterol (CH). The metabolism of C16:0, C16:1, C17:0, C18:0, C18:1, C18:2 and C18:3 in yak subcutaneous fat was mainly realized under the regulation of the INSIG1, ACACA, FASN, ELOVL6 and SCD genes, and TG synthesis was regulated by the AGPAT2 and DGAT2 genes. This study will provide a theoretical basis for yak genetic breeding and healthy feeding.

Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats

The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm^-1), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.

Analysis of milk with liquid chromatography–mass spectrometry: a review

As a widely consumed foodstuff, milk and dairy products are increasingly studied over the years. At the present time, milk profiling is used as a benchmark to assess the properties of milk. Modern biomolecular mass spectrometers have become invaluable to fully characterize the milk composition. This review reports the analysis of milk and its components using liquid chromatography coupled with mass spectrometry (LC–MS). LC–MS analysis as a whole will be discussed subdivided into the major constituents of milk, namely, lipids, proteins, sugars and the mineral fraction.

"The Yak"-A remarkable animal living in a harsh environment: An overview of its feeding, growth, production performance, and contribution to food security

Yaks play an important role in the livelihood of the people of the Qinghai-Tibet Plateau (QTP) and contribute significantly to the economy of the different countries in the region. Yaks are commonly raised at high altitudes of ~ 3,000-5,400 m above sea level. They provide many important products, namely, milk, meat, fur, and manure, as well as social status, etc. Yaks were domesticated from wild yaks and are present in the remote mountains of the QTP region. In the summer season, when a higher quantity of pasture is available in the mountain region, yaks use their long tongues to graze the pasture and spend ~ 30-80% of their daytime grazing. The remaining time is spent walking, resting, and doing other activities. In the winter season, due to heavy snowfall in the mountains, pasture is scarce, and yaks face feeding issues due to pasture scarcity. Hence, the normal body weight of yaks is affected and growth retardation occurs, which consequently affects their production performance. In this review article, we have discussed the domestication of yaks, the feeding pattern of yaks, the difference between the normal and growth-retarded yaks, and also their microbial community and their influences. In addition, blood biochemistry, the compositions of the yaks' milk and meat, and reproduction are reported herein. Evidence suggested that yaks play an important role in the daily life of the people living on the QTP, who consume milk, meat, fur, use manure for fuel and land fertilizer purposes, and use the animals for transportation. Yaks' close association with the people's well-being and livelihood has been significant.

Major Causes of Variation of External Appearance, Chemical Composition, Texture, and Color Traits of 37 Categories of Cheeses

Cheeses are produced by many different procedures, giving rise to many types differing in ripening time, size, shape, chemical composition, color, texture, and sensory properties. As the first step in a large project, our aim was to characterize and quantify the major sources of variation in cheese characteristics by sampling 1050 different cheeses manufactured by over 100 producers and grouped into 37 categories (16 with protected designation of origin, 4 traditional cheese categories, 3 pasta filata cheese categories, 5 flavored cheese categories, 2 goat milk categories, and 7 other categories ranging from very fresh to very hard cheeses). We obtained 17 traits from each cheese (shape, height, diameter, weight, moisture, fat, protein, water soluble nitrogen, ash, pH, 5 color traits, firmness, and adhesiveness). The main groups of cheese categories were characterized and are discussed in terms of the effects of the prevalent area of origin/feeding system, species of lactating females, main cheese-making technologies, and additives used. The results will allow us to proceed with the further steps, which will address the interrelationships among the different traits characterizing cheeses, detailed analyses of the nutrients affecting human health and sensorial fingerprinting.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect's allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

Comparative evaluation of miniaturized and conventional NIR spectrophotometer for estimation of fatty acids in cheeses

The miniaturization of near-infrared spectrometers has been growing rapidly. Several designs are now available, but there is a lack of understanding of how spectral data from these designs are affected by complex matrices and what are the limitations when compared to established systems. This study compares a popular miniaturized NIR device based on Hadamard-transform spectrometer (named miniaturized NIR) with a system based on dispersive spectrometer (named handheld-NIR) to assess: 1) their predictive performance; 2) the effect of a complex matrix on the performance, and 3) ability to discriminate multiples compounds in that matrix. The devices were challenged with a wide range of cheese types (n = 36) from different species (cow, goat, ewe and buffalo), brands (n = 30), countries of origin (n = 9) and with a broad range of cheese matrices (soft, fresh, semi-hard, hard and aged) to predict fat composition. Spectra were collected non-invasively with no sample preparation. Three wavelength ranges from handheld NIR were compared to miniaturized NIR based on two modelling approaches were used: a linear (Partial Least Square - PLS) and a non-linear (Support Vector Machine - SVM). The important wavelengths for each model were identified and used to assess the ability of the spectral data to differentiate among fatty acids. The highest prediction performance was observed for saturated fatty acids (C4.0, C14.0, C15.0 C16.0, total SCF and total SFA) with the RPD_EXT-VAL for the external validation dataset presenting values higher than 3 and the coefficient of determination for the external validation dataset (R²_EXT-VAL) higher than 0.89, mostly for SVM models. The sum of fatty acids also shows good prediction performance with RPD_EXT-VAL higher than 3 and R²_EXT-VAL higher than 0.89. Models with RPD_EXT-VAL between 2 and 3 includes: C6.0; C17.0; C18.0; C10.1; C16.1; C17.1; iso.C15.0; iso.C.16; iso.C17; C18.1.c11; C18.1.c9; anteiso C17; total MUFA; and total BCFA. The cheese matrix affected the linearity between spectral data and fatty acids concentration requiring a more complex model (SVM), but this effect was not enhanced by the instrument type. It was shown that the spectral information allows discrimination among fatty acids and this ability was not affected by the type of instrument. These findings demonstrated that the miniaturized NIR can be directly applied to a cheese matrix to monitor fatty acid composition with results equivalent to an optical-based design.

Balanced branched-chain amino acids modulate meat quality by adjusting muscle fiber type conversion and intramuscular fat deposition in finishing pigs

BACKGROUND

Pork is an important food for humans and improving the quality of pork is closely related to human health. This study was designed to investigate the effects of balanced branched-chain amino acid (BCAA)-supplemented protein-restricted diets on meat quality, muscle fiber types, and intramuscular fat (IMF) in finishing pigs.

RESULTS

The results showed that, compared with the normal protein diet (160 g kg-1 crude protein), the reduced-protein diet (120 g kg-1 crude protein) supplemented with BCAAs to the ratio of 2:1:2 not only had higher average daily gain (P < 0.05) and carcass weight (P < 0.05) but also improved meat tenderness and juiciness by decreasing shear force (P < 0.05) and increasing water-holding capacity (P < 0.05). In particular, this treatment showed higher (P < 0.05) levels of phospho-acetyl-CoA carboxylase (P-ACC) and peroxisome proliferation-activated receptor-γ (PPARγ), and lower (P < 0.05) levels of P-adenosine 5'-monophosphate (AMP)-activated protein kinase (P-AMPK), increasing the composition of IMF and MyHC I (P < 0.05) in the longissimus dorsi muscle (LDM). In terms of health, this group increased eicosapentaenoic acid (EPA) (P < 0.01) and desirable hypocholesterolemic fatty acids (DHFA) (P < 0.05), and decreased atherogenicity (AI) (P < 0.01) and hypercholesterolemic saturated fatty acids (HSFA) (P < 0.05).

CONCLUSION

Our findings suggest a novel role for a balanced BCAA-supplemented restricted protein (RP) diet in the epigenetic regulation of more tender and healthier pork by increasing IMF deposition and fiber type conversion, providing a cross-regulatory molecular basis for revealing the nutritional regulation network of meat quality. © 2021 Society of Chemical Industry.

The Application of FTIR Spectroscopy and Chemometrics for the Authentication Analysis of Horse Milk

Expensive milk such as horse's milk (HM) may be the target of adulteration by other milk such as goat's milk (GM) and cow's milk (CM). FTIR spectroscopy in combination with chemometrics of linear discriminant analysis (LDA) and multivariate calibrations of partial least square regression (PLSR) and principal component regression (PCR) was used for authentication of HM from GM and CM. Milk was directly subjected to attenuated total reflectance (ATR) spectral measurement at midinfrared regions (4000-650 cm-1). Results showed that LDA could make clear discrimination between HM and HM adulterated with CM and GM without any misclassification observed. PLSR using 2nd derivative spectra at 3200-2800 and 1300-1000 cm-1 provided the best model for the relationship between actual values of GM and FTIR predicted values than PCR. At this condition, R 2 values for calibration and validation models obtained were 0.9995 and 0.9612 with RMSEC and RMSEP values of 0.0093 and 0.0794. PLSR using normal FTIR spectra at 3800-3000 and 1500-1000 cm-1 offered R 2 for the relationship between actual values of CM and FTIR predicted values of >0.99 in calibration and validation models with low errors of RMSEC of 0.0164 and RMSEP of 0.0336 during authentication of HM from CM. Therefore, FTIR spectroscopy in combination with LDA and PLSR is an effective method for authentication of HM from GM and CM.

Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixOmics methods for profile discrimination: Application case on cheese

Volatile organic compounds (VOCs) emitted by food products are decisive for the perception of aroma and taste. The analysis of gaseous matrices is traditionally done by detection and quantification of few dozens of characteristic markers. Emerging direct injection mass spectrometry technologies offer rapid analysis based on a soft ionisation of VOCs without previous separation. The recent increase of selectivity offered by the use of several precursor ions coupled with untargeted analysis increases the potential power of these instruments. However, the analysis of complex gaseous matrix results in a large number of ion conflicts, making the quantification of markers difficult, and in a large volume of data. In this work, we present the exploitation of untargeted SIFT-MS volatile fingerprints of ewe PDO cheeses in a real farm model, using mixOmics methods allowing us to illustrate the typicality, the manufacturing processes reproducibility and the impact of the animals' diet on the final product.

Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese

Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.

Authenticity of Hay Milk vs. Milk from Maize or Grass Silage by Lipid Analysis

Hay milk is a traditional dairy product recently launched on the market. It is protected as “traditional specialty guaranteed” (TSG) and subjected to strict regulations. One of the most important restrictions is that the cow’s feed ration must be free from silage. There is the need for analytical methods that can discriminate milk obtained from a feeding regime including silage. This study proposes two analytical approaches to assess the authenticity of hay milk. Hay milk and milk from cows fed either with maize or grass silage were analyzed by targeted GC-MS for cyclopropane fatty acid (dihydrosterculic acid, DHSA) detection, since this fatty acid is strictly related to the bacterial strains found in silage, and by HPLC-HRMS. The presence of DHSA was correlated to the presence of maize silage in the feed, whereas it was ambiguous with grass silage. HPLC-HRMS analysis resulted in the identification of 14 triacylglycerol biomarkers in milk. With the use of these biomarkers and multivariate statistical analysis, we were able to predict the use of maize and grass silage in the cow’s diet with 100% recognition. Our findings suggest that the use of analytical approaches based on HRMS is a viable authentication method for hay milk.

Graduate Student Literature Review: Current understanding of the influence of on-farm factors on bovine raw milk and its suitability for cheesemaking

Relationships between dairy farm practices, the composition and properties of raw milk, and the quality of the resulting cheese are complex. In this review, we assess the effect of farm factors on the quality of bovine raw milk intended for cheesemaking. The literature reports several prominent farm-related factors that are closely associated with milk quality characteristics. We describe their effects on the composition and technological properties of raw milk and on the quality of the resulting cheese. Cow breed, composite genotype, and protein polymorphism all have noticeable effects on milk coagulation, cheese yield, and cheese composition. Feed and feeding strategy, dietary supplementation, housing and milking system, and seasonality of milk production also influence the composition and properties of raw milk, and the resulting cheese. The microbiota in raw milk is influenced by on-farm factors and by the production environment, and may influence the technological properties of the milk and the sensory profile of certain cheese types. Advances in research dealing with the technological properties of raw milk have undoubtedly improved understanding of how on-farm factors affect milk quality attributes, and have refuted the concept of one milk for all purposes. The specific conditions for milk production should be considered when the milk is intended for the production of cheese with unique characteristics. The scientific identification of these conditions would improve the current understanding of the complex associations between raw milk quality and farm and management factors. Future research that considers dairy landscapes within broader perspectives and develops multidimensional approaches to control the quality of raw milk intended for long-ripening cheese production is recommended.

Fat Deposition in the Muscle of Female and Male Yak and the Correlation of Yak Meat Quality with Fat

This study aimed to explore the differences in fat deposition between female (FYs) and male yaks (MYs). Compared with MYs, the tenderness, L*, marbling, absolute content of fat, and most fatty acids (FAs) of longissimus dorsi (LD) in FYs were higher or better (p < 0.05), whereas the relative content of polyunsaturated fatty acids (PUFAs) and n-3 PUFAs were lower (p < 0.01). The absolute content of fat, C18:0, cis-C18:2, cis-C18:1, and C24:0 were positively correlated with L*_{45 min}, b*_{24 h}, tenderness, and marbling score of LD in FYs and MYs (p < 0.05), respectively. LPL, FATP2, ELOVL6, HADH, HACD, and PLINS genes play a crucial role in improving the marbling score and tenderness of yak meat. The results of gene expression and protein synthesis showed the effect of gender to FA biosynthesis, FA transport, lipolysis, and FA oxidation in the adipose tissue of yak was realized by the expressions of ME1, SCD, ACSL5, LPL, FABP1, PLIN4, and PLIN2 in peroxisome proliferators-activated receptor (PPAR) signaling. This study established a theoretical basis for the improvement of the meat quality of yak and molecular breeding.

Phenotypic and genetic variation of ultraviolet-visible-infrared spectral wavelengths of bovine meat

Spectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet-visible and near-infrared region (UV-Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV-Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV-Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.

The Combination of Vibrational Spectroscopy and Chemometrics for Analysis of Milk Products Adulteration

Milk products obtained from cow, goat, buffalo, sheep, and camel as well as fermented forms such as cheese, yogurt, kefir, and butter are in a category of the most nutritious foods due to their high contents of high protein contributing to total daily energy intake. For certain reasons, high price milk products may be adulterated with low-quality ones or with foreign substances such as melamine and formalin which are added into them; therefore, a comprehensive review on analytical methods capable of detecting milk adulteration is needed. The objective of this narrative review is to highlight the use of vibrational spectroscopies (near infrared, mid infrared, and Raman) combined with multivariate analysis for authentication of milk products. Articles, conference reports, and abstracts from several databases including Scopus, PubMed, Web of Science, and Google Scholar were used in this review. By selecting the correct conditions (spectral treatment, normal versus derivative spectra at wavenumbers region, and chemometrics techniques), vibrational spectroscopy is a rapid and powerful analytical technique for detection of milk adulteration. This review can give comprehensive information for selecting vibrational spectroscopic methods combined with chemometrics techniques for screening the adulteration practice of milk products.

Determining the geographical origin of milk by multivariate analysis based on stable isotope ratios, elements and fatty acids

To construct a reliable discrimination model for determining milk geographical origin, stable isotope ratios including δ¹³C, δ¹⁵N and δ¹⁸O, 51 elements and 35 fatty acids (FAs) in milk samples from Australia, New Zealand and Austria were detected and analyzed. It is found that all of the stable isotope ratios in the milk samples of Australia are the highest, followed by those of the samples from New Zealand and Austria. In addition, 14 elements and 8 FAs show different contents in the samples of different countries at the significance level of P < 0.05. Based on these results, a multivariate model with good robustness and predictive ability for authenticating milk origin (R²X = 0.693, Q² = 0.854) was successfully constructed. Element contents and stable isotope ratios are more reliable variables for milk origin discrimination and Rb, δ¹⁸O, Tl, Ba, Mo, Sr, δ¹⁵N, Cs, As, Eu, C20:4n6, Sc, C13:0, K, Ca and C16:1n7 are the critical markers in the multivariate model for verifying milk origin.

Invited review: A 2020 perspective on pasture-based dairy systems and products

Grazing pasture is the basis for dairy production systems in regions with temperate climates, such as in Ireland, New Zealand, parts of Australia, the United States, and Europe. Milk and dairy products from cows on pasture-based farms predominantly consuming fresh grazed grass (typically classified as "grass-fed" milk) have been previously shown to possess a different nutrient profile, with potential nutritional benefits, compared with conventional milk derived from total mixed ration. Moreover, pasture-based production systems are considered more environmentally and animal welfare friendly by consumers. As such, there is significant potential for market capitalization on grass-fed dairy products. As competition in this space increases, the regulations of what constitutes as grass-fed vary between different regions of the world. With this in mind, there is a need for clear and independently accredited grass-fed standards, defining the grass-fed criteria for labeling of products as such, subsequently increasing the clarity and confidence for the consumer. This review outlines the numerous effects of pasture production systems on dairy product composition, nutritional profile, and sustainability, and highlights potential future methods for authentication.

Using Sensory Wheels to Characterize Consumers' Perception for Authentication of Taiwan Specialty Teas

In the context of fair trade and protection of consumer rights, the aim of this study was to combat adulteration, counterfeiting, and fraud in the tea market, and rebuild the image of high-quality Taiwan teas. Experts at the Tea Research and Extension Station, Taiwan (TRES), are engaged in promotion of the systems of origin identification (AOC) and grading for authentication of Taiwan's premium teas. From tea evaluation competitions (bottom-up quality campaign), the flavor descriptions and consumers' perceptions were deconvoluted and characterized for the eight Taiwan specialty teas, namely, Bi-Luo-Chun, Wenshan Paochong, High-Mountain Oolong, Dongding Oolong, Tieh-Kuan-Yin, Red Oolong, Oriental Beauty, and Taiwan black tea. Then, according to the manufacturing processes, producing estates and flavor characters, the specialty teas were categorized into six sensory wheels. The flavor descriptors of the sensory wheels were also recognized in consumers' feedback. In recent years, the performance of international trade in tea also demonstrates that the policy guidelines for authentication of specialty teas are helpful to the production and marketing. Furthermore, the development of sensory wheels of Taiwan's specialty teas is the cornerstone to the establishment of the Taiwan-tea assortment and grading system (TAGs) for communication with the new generation consumers, enthusiasts, sellers, and producers.

Can FT-Mid-Infrared Spectroscopy of Milk Samples Discriminate Different Dietary Regimens of Sheep Grazing With Restricted Access Time?

Milk obtained from sheep grazing natural pastures and some forage crops may be worth a plus value as compared to milk obtained from stall-fed sheep, due to their apparently higher content of beneficial fatty acids (FAs). Fourier transformed mid-infrared (FT-MIR) analysis of FA can help distinguish milk from different areas and diverse feeding systems. The objective was to discriminate milk from sheep and milk from dairy sheep rotationally grazing Italian ryegrass or berseem clover for 2, 4, or 6 h/day. To test this hypothesis, a data-mining study was undertaken using a database of 1,230 individual milk spectra. Data were elaborated by principal component analysis (PCA) and analyzed by linear discriminant analysis (LDA) with or without the use of genetic algorithm (GA) as a variable selection tool with the primary aim to discriminate grazed forages (grass vs. legume), access time (2, 4, or 6 h/day), grazing day (first vs. last grazing day during the 7-day grazing period), and the milking time (morning vs. afternoon milking). The best-fitting discriminant models of FT-MIR spectra were able to correctly predict 100% of the samples differing for the pasture forage, 91.9% of the samples differing for grazing day, and 97.1% of the samples regarding their milking time. The access time (AT) to pasture was correctly predicted by the model in 60.3% of the samples, and the classification ability was improved to 77.0% when considering only the 2 and 6 h/day classes.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

Prediction of indicators of cow diet composition and authentication of feeding specifications of Protected Designation of Origin cheese using mid-infrared spectroscopy on milk

The ability of mid-infrared spectroscopy (MIR) to predict indicators (1) of diet composition in dairy herds and (2) for the authentication of the cow feeding restrictions included in the specification of 2 Protected Designation of Origin (PDO) cheeses (Cantal and Laguiole) was tested on 7,607 bulk milk spectra from 1,355 farms located in the Massif Central area of France. For each milk sample, the corresponding cow diet composition data were obtained through on-farm surveys. The cow diet compositions varied largely (i.e., from full grazing for extensive farming systems to corn silage-based diets, which are typical of more intensive farming systems). Partial least square regression and discriminant analysis were used to predict the proportion of different feedstuffs in the cows' diets and to authenticate the cow feeding restrictions for the PDO cheese specifications, respectively. The groups for the discriminant analysis were created by dividing the data set according to the threshold of a specific feedstuff. They were issued based on the specifications of the restriction of the PDO cheese. The pasture proportion in the cows' diets was predicted by MIR with an coefficient of determination in external validation (R²V) = 0.81 and a standard error of prediction of 11.7% dry matter. Pasture + hay, corn silage, conserved herbage, fermented forage, and total herbage proportion in the cows' diets were predicted with a R²V >0.61 and a standard error of prediction <14.8. The discrimination models for pasture presence, pasture ≥50%, and pasture ≥57% in the cows' diets achieved an accuracy and specificity ≥90%. A sensitivity and precision ≥85% were also observed for the pasture proportion discrimination models, but both of these indexes decreased at increasing thresholds from 0 to 50, and 57% pasture in the cows' diets. An accuracy ≥80% was also observed for pasture + hay ≥72%, herbage ≥50%, pasture + hay ≥25%, absence of fermented herbage, absence of corn silage, and corn silage ≤30% in the cows' diets, but for several models, either the sensitivity or precision was lower than the accuracy. Models built on the simultaneous respect of all the criteria of the feeding restrictions of PDO cheese specifications achieved an accuracy, specificity, sensitivity, and precision >90%. Both the regression and discriminant MIR models for bulk milk can provide useful indicators of cow diet composition and PDO cheese specifications to producers and consumers (farmers, dairy plants).

Predicting the Content of 20 Minerals in Beef by Different Portable Near-Infrared (NIR) Spectrometers

The aim of this study was to test the predictability of a detailed mineral profile of beef using different portable near-infrared spectrometers (NIRS). These devices are rapid, chemical waste-free, cheap, nondestructive tools that can be used directly on the meat surface in the work environment without the need to take samples. We compared a transportable Visible-NIRS (weight 5.6 kg; wavelength 350-1830 nm), a portable NIRS (2.0 kg; 950-1650 nm), and a hand-held Micro-NIRS (0.06 kg; 905-1649 nm) to predict the contents of 20 minerals (measured by ICP-OES) in 178 beef samples (Longissimus thoracis muscle) using different mathematical pretreatments of the spectra and partial least square regressions. The externally validated results show that Fe, P, Mg, S, Na, and Pb have some potential for prediction with all instruments (R2VAL: 0.40-0.83). Overall, the prediction performances of the three instruments were similar, although the smallest (Micro-NIRS) exhibited certain advantages.

Prediction of meat quality traits in the abattoir using portable and hand-held near-infrared spectrometers

The use of near-infrared spectrometers (NIRS) for predicting meat quality traits directly in the abattoir was tested with three trials. For the calibration trial, spectra were acquired from the cross-cut surface of the Longissimus thoracis muscle on 1166 carcasses of Piemontese young bulls with a portable visible-near-infrared spectrometer (Vis-NIRS) and with a small hand-held instrument (Micro-NIRS). A sample of the same muscle was analyzed to provide the reference. Validation statistics of the two instruments were similar. Predictabilities of meat color and purge loss were good, whereas for the other traits they were less promising. The repeatability trial showed that post-slaughter factors, not predictable by NIR spectra collected in the abattoir, affect reference meat quality values. A trial under operative conditions showed that both spectrometers were able to capture the major sources of variation in most of the meat quality traits. Overall, NIRS could be used to predict the animals' "native" characteristics exploitable for genetic improvement of meat quality traits.