Authentication and Quality Assessment of Meat Products by Fourier-Transform Infrared (FTIR) Spectroscopy

Application of UV-Vis-NIR and FTIR spectroscopy coupled with chemometrics for quality prediction of katsuobushi based on the number of smoking treatments

Katsuobushi, a smoked, dried skipjack tuna, is a traditional Japanese food additive with a unique flavor and taste. Gas chromatography mass spectrometry (GC-MS), fourier transform infrared (FTIR), and ultraviolet-visible-near infrared spectroscopy (UV-Vis-NIR) combined with chemometric methods were evaluated the quality of katsuobushi according to the number of smoking treatments. Using GC-MS, 46 metabolites were identified and five metabolites were selected as key compounds. All samples were classified according to their smoking number via principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and hierarchical cluster analysis (HCA) of the FTIR and NIR spectra. Partial least squares regression (PLSR) analysis revealed that the FTIR and NIR spectra were highly correlated with the metabolites by GC-MS. These results demonstrated the potential of using the FTIR and NIR spectroscopy combined with chemometrics to assess the quality of katsuobushi based on the smoking treatments, with NIR spectroscopy showed particularly promising.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

The fractional analysis of thermo-elasticity coupled systems with non-linear and singular nature

It is mentioned that understanding linear and non-linear thermo-elasticity systems is important for understanding temperature, elasticity, stresses, and thermal conductivity. One of the most crucial aspects of the current research is the solution to these systems. The fractional form of several thermo-elastic systems is explored, and elegant solutions are provided. The solutions of fractional and integer thermo-elastic systems are further discussed using tables and diagrams. The closed contact between the LRPSM and exact solutions is displayed in the graphs. Plotting fractional problem solutions demonstrates their convergence towards integer-order problem solutions for suitable modeling. The tables confirm that greater precision is rapidly attained as the terms of the derived series solution increase. The faster convergence and stability of the suggested method support its modification for other fractional non-linear complex systems in nature.

Study on the thawing characteristics of beef in ultrasound combined with plasma-activated water

The effects of deionized water thawing (DT), plasma-activated water thawing (PT), ultrasound (150 W, 40 kHz) combined with deionized water thawing (UDT), and ultrasound combined with plasma-activated water thawing (UPT) on the thawing characteristics and the physicochemical properties of the beef were investigated. The results showed that the UPT group had a faster thawing rate (38 % higher compared to the PT group) and good bactericidal ability (75 % higher compared to the UDT group), and had no adverse effect on the color and pH value of the beef. Plasma-activated water (PAW) can maintain the stability of the beef fiber, improve the water holding capacity (WHC), inhibit lipid oxidation, and reduce the loss of soluble substances such as protein. Therefore, UPT thawing is a promising meat thawing technology, which provides practical guidance and methods for the wide application of UPT in the field of meat thawing.

Evaluation of the Quality Changes in Three Commercial Pastourma Samples during Refrigerated Storage Using Physicochemical, Microbiological, and Image Analyses Combined with Chemometrics

Despite the inherent stability of dried and cured products, such as pastourma, appropriate refrigeration remains essential for preserving their optimal characteristics. This study explored quality and safety characteristics in lamb, beef, and buffalo pastourma during 16-day refrigeration storage after package opening. The comprehensive approach employed Attenuated Total Reflection-Fourier-Transform Infrared (ATR-FTIR) spectroscopy, colorimetry, and image analysis, alongside physicochemical and microbiological analyses, to shed light on these alterations. The findings reveal a reduction in textural uniformity and color vibrancy (fading reds and yellows) across all samples during storage, with lamb pastourma exhibiting the most pronounced effects. Notably, image analysis emerged as a powerful tool, enabling the accurate classification of samples based on storage duration. Additionally, significant variations were observed in moisture content, hue angle, firmness, and TBARS levels, highlighting their influence on pastourma quality. The study documented a gradual decrease in lactic acid bacteria and aerobic plate count populations over time. ATR-FTIR spectra's interpretation revealed the presence of lipids, proteins, carbohydrates, and water. Protein secondary structures, demonstrably influenced by the meat type used, exhibited significant changes during storage, potentially impacting the functional and textural properties of pastourma. Overall, the findings contribute to a deeper understanding of pastourma spoilage during storage, paving the way for the development of improved preservation and storage strategies.

Fingerprinting based on spectral reflectance and chemometrics - An analytical approach aimed at combating the illegal trade of stingray meat in the Amazon

The survival of Amazon stingrays is threatened due to excessive fishing and habitat degradation. To address this issue, this study developed a groundbreaking method to authenticate and differentiate Amazon stingray meats using a portable spectrophotometer and chemometrics. Samples were collected from various species, including an endangered one with a commercialization ban and no population reduction records. Principal Component Analysis (PCA), identified natural groupings based on the meat's commercial origin, while Partial Least Squares-Discriminant Analysis (PLS-DA), accurately discriminated the commercial and geographic origins with 100 % accuracy. Moreover, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), effectively distinguished Amazon stingray meat from other marketable species. This approach offers a rapid, precise, and non-destructive means for monitoring and controlling the illegal trade of these species, thereby supporting decision-making in the field and promoting the conservation and sustainability of freshwater stingrays in the Amazon region.

Effect of high pressure pretreatment on the inhibition of ice nucleation and biochemical changes in pork loins during supercooling preservation

In this study, the effect of high pressure (HP) pretreatment on the stability of pork loins during supercooling (SC) preservation was investigated, and the freshness and postmortem metabolism of pork loins preserved by SC was evaluated. Based on the differential scanning calorimetry (DSC), the peak enthalpies of 200 MPa treatment were lower than those of 50 MPa treatment (P < 0.05). For the nuclear magnetic resonance (NMR) relaxometry, extramyofibrillar water in pork loins was decreased with increasing intermyofibrillar water at >100 MPa (P < 0.05). Compared to unpressurized control all HP treatment had less α-helix structure while random coil was dominated from the Fourier transform infrared (FTIR) spectroscopy (P < 0.05). A 200 MPa was selected to estimate the relationship between HP pretreatment and stability of SC preservation of pork loins. The HP-treated pork loins showed high stability during SC preservation under the relatively low temperature algorithm. Compared to fresh control, HP pretreatment caused physicochemical changes of pork loins which did not recover even after 2 weeks of preservation. Nevertheless, HP followed by SC preservation was able to reduce property changes better than pork loins preserved by normal refrigeration. According to the analyses of transmission electron microscopy (TEM), the HP pretreatment influenced the postmortem biochemical metabolism of pork loins, however, it did not affect the freshness and quality parameters of pork loins due to the subsequently applied low preservation temperature of SC. Therefore, this study demonstrated that moderate HP pretreatment was a potential pretreatment for SC preservation of pork loins.

Influence of electrohydrodynamics on the drying characteristics and volatile components of iron stick yam

The drying characteristics, rehydration capacity, color, infrared spectra and volatile components of iron stick yam slices were investigated under different alternating current (AC) voltages (13, 17, 21 kV), hot air drying (HAD) (60 °C) and natural drying (AD) by electrohydrodynamic (EHD) drying and HAD experimental devices. The results showed that slices of iron stick yam dried the quickest with HAD, which also had the fastest drying rate; while drying the slices of iron stick yam with EHD led to a better rehydration capacity, higher brightness L* and whiteness, a more stable protein secondary structure, and a greater variety and content of volatile components compared with AD and HAD. These finding indicated that EHD is a more promising method for drying iron stick yam.

Studying the metabolic factors that may impact the growth of co-cultured Listeria monocytogenes strains at low temperature

The simultaneous presence of more than one strains of Listeria monocytogenes in the same food product may affect the growth capacity of each strain. The present study evaluated the metabolites composition that may potentially influence the growth of individual L. monocytogenes strains in a dual strain composite. Based on previous studies, L. monocytogenes strains, C5 (4b) and 6179 (1/2a) were selected due to the remarkable interaction, which was observed during their co-culture. The selected strains were inoculated (2.0 - 3.0 log CFU/mL) in Tryptic Soy Broth with 0.6% Yeast Extract (TSB-YE) in single and two-strain cultures (1:1 strain ratio). Bacterial growth was assessed during storage at 7 °C, under aerobic conditions (AC). Their resistance to different antibiotics enabled the selective enumeration of each strain in the co-culture. After reaching stationary phase, single and dual cultures were centrifuged and filtered. The cell-free spent medium (CFSM) was either characterized by Fourier transform infrared (FTIR-ATR) spectrometry or re-inoculated, after the addition of concentrated TSB-YE (for nutrient replenishment), with single and two-strain cultures for the evaluation of growth under the influence of metabolites produced from the same singly and co-cultured strains in the different combinations of strains and CFSM origin (7 °C/AC) (n = 2x3). By the end of storage, singly-cultured C5 and 6179 had reached 9.1 log CFU/mL, while in dual culture, 6179 was affected by the presence of C5 attaining only 6.4 ± 0.8 log CFU/mL. FTIR-ATR spectra of CFSM produced by singly-cultured 6179 and the co-culture were almost identical. Characteristic peaks in FTIR-ATR spectrum of CFSM of singly-cultured C5 at 1741, 1645 and 1223 cm^-1 represent functional groups which were not present in the CFSM of the co-culture. These molecules may be located intracellularly or mounted on bacterial cell surface and removed from the supernatant during cell filtration of the co-culture. Both singly- and co-cultured 6179 managed to grow similarly regardless of CFSM origin. Contrarily, both singly- and co-cultured C5 managed to outgrow 6179 in CFSM which contained high concentration of C5 metabolites, while in CFSM produced by singly-cultured 6179, C5 did not grow, suggesting that the produced metabolites of strain 6179 appears to be harmful to strain C5. However, during co-culture, C5 may produce molecules that counteract the inhibitory effect of 6179. The findings shed more light on the mechanism behind the inter-strain interactions of L. monocytogenes indicating that both contact of cells and extracellular metabolites may influence the behavior of the different co-existing strains.

Interference with Bacterial Conjugation and Natural Alternatives to Antibiotics: Bridging a Gap

Horizontal gene transfer (HGT) in food matrices has been investigated under conditions that favor gene exchange. However, the major challenge lies in determining the specific conditions pertaining to the adapted microbial pairs associated with the food matrix. HGT is primarily responsible for enhancing the microbial repertoire for the evolution and spread of antimicrobial resistance and is a major target for controlling pathogens of public health concern in food ecosystems. In this study, we investigated Salmonella Heidelberg (SH) and Escherichia coli (EC) regarding gene exchange under conditions mimicking the industrial environment, with the coproducts whey (SL) and chicken juice (CJ). The S. Heidelberg strain was characterized by antibiotic susceptibility standards and PCR to detect the bla_TEM gene. A concentration of 0.39 mg/mL was determined to evaluate the anti-conjugation activity of nanostructured lipid nanocarriers (NLCs) of essential oils to mitigate β-lactam resistance gene transfer. The results showed that the addition of these coproducts promoted an increase of more than 3.5 (whey) and 2.5 (chicken juice) orders of magnitude in the conjugation process (p < 0.01), and NLCs of sage essential oil significantly reduced the conjugation frequency (CF) by 74.90, 90.6, and 124.4 times when compared to the transfers in the absence of coproducts and the presence of SL and CJ, respectively. For NLCs from olibanum essential oil, the decrease was 4.46-fold for conjugations without inhibitors and 3.12- and 11.3-fold in the presence of SL and CJ. NLCs associated with sage and olibanum essential oils effectively control the transfer of antibiotic resistance genes and are a promising alternative for use at industrial levels.

Determination of quality traits and possible adulteration of molasses using FT-IR spectroscopy: A study from Turkish market

We aimed to develop portable Fourier transform infrared (FT-IR) spectroscopy-based prediction algorithms for the key quality characteristics (soluble solids, water activity, pH, sucrose, glucose, fructose, fructose/glucose, hydroxymethylfurfural) of various types of molasses, establish their legitimacy, and create a model to separate them based on their botanical origin. Samples labeled as carob (n = 27), grape (n = 24), Juniper (n = 13), and mulberry (n = 12) were purchased from different local markets in Turkey. Labeling issues were revealed in five carob and seven grape molasses, and those samples classified as non-authentic by the FT-IR algorithms were corroborated by reference analysis. Partial least squares regression models generated to predict the key quality traits of Turkish molasses demonstrated excellent correlation with reference analysis (R²_Val ≥ 0.96) and low standard error of prediction (SEP ≤ 2.88). The FT-IR sensor provided a feasible approach for molasses testing to assess its quality through manufacturing and storage, also provided a powerful tool to -ensure proper product labeling.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

Monitoring the curing efficiency of a plant-based green engineered additive on caprine skin: a spectroscopic and chemometric novel approach

Environmental sustainability in industrial processing is one of the most fundamental requirements for sustainable development. The leather industry is known for its vast environmental pollution. But, green engineering may occur a paradigm shift in this sector. Plant-based goatskins curing is a cutting-edge green technology that holds the concept of pollution reduction through prevention on the upper stream of leather processing. The successful and rapid monitoring of the efficiency of this technology is the foremost demand for mass-level applications. In this study, ATR-FTIR spectroscopy was employed to monitor this technology's efficiency using a plant Polygonum hydropiper. Chemometrics was also applied to extract information from spectral data leading to an understanding of the inherent effect of studying preservatives on goatskins collagen chemistry. 10% plant-paste +5% NaCl, 10% plant-paste +10% NaCl, and 15% plant-paste +5% NaCl on goatskin were assessed by ATR-FTIR on 0th, 10th, and 30th days of preservation. The Spectral peak fitting (R² = 0.99) area of amide I and II of collagen peptide bands revealed 2.73 to 1.33 times more structural suitability of studied goatskins than the control. Principal component analysis and Hierarchical cluster analysis showed that, after 30 days of curing, 15% paste +5% salt-rubbed goatskin collagen matrix significantly (around 50%) interacted with P. hydropiper. The interaction was superficial, as it happened before the opening up of collagen fibers. In conclusion, ATR-FTIR spectroscopy with Chemometrics can be an effective tool in evaluating the efficiency of goatskin curing and understanding the entire effect on collagen chemistry quickly.

Heating temperatures affect meat quality and vibrational spectroscopic properties of slow- and fast-growing chickens

This study determined the effect of water bath cooking (70°C and 90°C for 40 min) and the extreme heat treatment by an autoclave (121°C for 40 min) on the quality of breast meat of a fast-growing chicken, commercial broiler (CB), and slow-growing chickens, Korat chicken (KC), and Thai native chicken (NC) (Leung Hang Khao), by vibrational spectroscopic techniques, including synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy and Fourier transform Raman (FT-Raman) spectroscopy. Taste-enhancing compounds, including inosine-5'-monophosphate (IMP) and guanosine-5'-monophosphate (GMP), were better retained in cooked KC and NC meats than in cooked CB meat (P < 0.05). The high heat treatment at 121°C depleted the amount of insoluble collagen in all breeds (P < 0.05). Shear force values of slow-growing chicken meat were not affected by high heating temperatures (P > 0.05). In addition, the high heat treatment increased protein carbonyl (P < 0.05), while no effect on in vitro protein digestibility (P > 0.05). SR-FTIR microspectroscopy performed better in differentiating the meat quality of different chicken breeds, whereas FT-Raman spectroscopy clearly revealed differences in meat qualities induced by heating temperature. Based on principal component analysis (PCA), distinct characteristics of chicken meat cooked at 70°C were high water-holding capacity, lightness (L*), moisture content, and predominant α-helix structure, correlating with Raman spectra at 3,217 cm^-1 (O-H stretching of water) and 1,651 cm^-1 (amide I; α-helix). The high heating temperature at 90°C and 121°C exposed protein structure to a greater extent, as evidenced by an increase in β-sheets, which was well correlated with the Raman spectra at 2,968 and 2,893 cm^-1 (C-H stretching), tryptophan (880 cm^-1), tyrosine (858 cm^-1), and 1,042, 1,020, and 990 cm^-1 (C-C stretching; β-sheet). SR-FTIR and FT-Raman spectroscopy show potential for differentiation of chicken meat quality with respect to breeds and cooking temperatures. The marked differences in wavenumbers would be beneficial as markers for determining the quality of cooked meats from slow- and fast-growing chickens.

The sex impact on the technological and chemical characteristics of meat derived from the Polish native chicken breed

The aim of the study was to evaluate the birds' sex as well as the caponisation influence on the technological traits of obtained meat, fatty acids profile as well as main chemical compounds distribution. In this study, 40 hens, roosters and green-legged partridge capons each were used (4 replications each). At 24 weeks of age, 2 birds from each replication subgroup were selected and slaughtered. During dissection analysis, pectoral and femoral muscles were sampled. Acidity, colour, tenderness, water holding capacity, drip and cooking loss were determined in the obtained material. The fatty acid profile of the meat was also determined, as well as the distribution of components in the pressed meat samples using FTIR spectroscopy. Statistically significant differences in the colour lightness index (L*) of the breast muscles were recorded, with cockerel meat being the darkest and capon meat the lightest. The greatest natural water loss was recorded in the hens' pectoral muscle; cooking loss was also the greatest in these samples. Roosters showed significantly the lowest content of monounsaturated fatty acids, at the same time the highest proportion of the n3 fatty acids group was found in capons. Irrespective of muscle, meat from roosters showed the highest susceptibility to oxidation (PI index). The variation in the chemical composition of the meat was confirmed by FTIR mapping.

Rapid discrimination of Brucellosis in sheep using serum Fourier transform infrared spectroscopy combined with PCA-LDA algorithm

Brucellosis in sheep is an infectious disease caused by Brucella melitensis in sheep. The current conventional serological methods for screening Brucella-infected sheep have the disadvantage of time consuming and low accuracy, so a simple, rapid and highly accurate screening method is needed. The aim of this study was to evaluate the feasibility of diagnosing Brucella-infected sheep by serum samples based on the Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR spectroscopy of serum from Brucella-infected sheep (n=102) and healthy sheep (n=125) revealed abnormal protein and lipid metabolism in serum from Brucella-infected sheep compared to healthy sheep. Principal component analysis-Linear discriminant analysis (PCA-LDA) method was used to differentiate the FTIR spectra of serum from Brucella-infected sheep and healthy sheep in the protein band (3700-3090 cm^-1) and lipid band (3000-2800 cm^-1), and its overall diagnostic accuracy was 100% (sensitivity 100%, specificity 100%). In conclusion, our results suggest that serum FTIR spectroscopy combined with PCA-LDA algorithm has great potential for brucellosis in sheep screening.

Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content

Fourier transform mid infrared (FT-MIR) spectroscopy combined with modeling techniques has been studied as a useful tool for multivariate chemical analysis in agricultural research. A drawback of this method is the sample preparation requirement, in which samples must be dried and fine ground for accurate model calibrations. For research involving large sample sets, this may dramatically increase the time and cost of analysis. This study investigates the effect of fine grinding on model performance using leaf tissue from a variety of crop species. Dried leaf samples (N = 300) from various environmental conditions were obtained with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques. Scanning was repeated after fine grinding for 2, 5, and 10 min. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations. All analytes except for boron, iron, and zinc were well-modeled (average R² > 0.7), with higher R² values on ATR spectra. The 5 min level of fine grinding was found to be most optimal considering overall model performance and sample preparation time.

Conventional and advanced detection techniques of foodborne pathogens: A comprehensive review

Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results.

Preliminary investigation into the prediction of indicators of beef spoilage using Raman and Fourier transform infrared spectroscopy

The objective of this study was to assess the potential to predict the microbial beef spoilage indicators by Raman and Fourier transform infrared (FT-IR) spectroscopies. Vacuum skin packaged (VSP) beef steaks were stored at 0 °C, 4 °C, 8 °C and under a dynamic temperature condition (0 °C ∼ 4 °C ∼ 8 °C, for 36 d). Total viable count (TVC) and total volatile basic nitrogen (TVB-N) were obtained during the storage period along with spectroscopic data. The Raman and FTIR spectra were baseline corrected, pre-processed using Savitzky-Golay smoothing and normalized. Subsequently partial least squares regression (PLSR) models of TVC and TVB-N were developed and evaluated. The root mean squared error (RMSE) ranged from 0.81 to1.59 (log CFU/g or mg/100 g) and the determination coefficient (R²) from 0.54 to 0.75. The performance of PLSR model based on data fusion (combination of Raman and FT-IR data) is better than that based on Raman spectra and similar to that of FT-IR. Overall, Raman spectroscopy, FT-IR spectroscopy, and a combination of both exhibited a potential for the prediction of the beef spoilage.

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning

INTRODUCTION

The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing.

METHODS

In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels).

RESULTS

The results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small.

DISCUSSION

The findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.

Investigating the Effect of Various Sous-Vide Cooking Conditions on Protein Structure and Texture Characteristics of Tilapia Fillet Using Synchrotron Radiation-Based FTIR

The effects of various sous-vide (SV) cooking conditions (50-60℃, 30-60 min) on physicochemical properties related to the texture characteristics, protein structure/degradation, and sensory acceptability of tilapia fillet (Oreochromis niloticus) were investigated. With an increasing temperature and processing time of SV cooking, protein degradation (of both myofibrils and connective tissue) was more pronounced, as evaluated by the decrease in water- and salt-soluble proteins, total collagen, as well as the changes in the ratio of secondary protein structures (α-helix, β-sheet, β-turn, etc.), which were determined by synchrotron-FTIR (SR-FTIR). These degradations were associated with the improvement of meat tenderness, as estimated by shear force and texture profile analyzer (TPA) results. Among all SV conditions, using 60 ℃ for 45 min seems to be the optimal condition for tilapia meat, since it delivered the best results for texture characteristics and acceptability (p < 0.05). Moreover, principal component analysis (PCA) results clearly demonstrated that the highest texture-liking score of this condition was well associated with the intensity of β-sheets, which seem to be the crucial component that affected the texture of SV-cooked tilapia more so than other parameters. The findings demonstrated the potential of SR-FTIR to decipher the biomolecular structure, particularly the secondary protein structure, of SV-cooked tilapia. This technique provided essential information for a better understanding of the changes in biomolecules related to the textural characteristics of this product.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm^-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

The Analytical Possibilities of FT-IR Spectroscopy Powered by Vibrating Molecules

This paper discusses the state of advancement in the development of spectroscopic methods based on the use of mid (proper) infrared radiation in the context of applications in various fields of science and technology. The authors drew attention to the most important solutions specific to both spectroscopy itself (ATR technique) and chemometric data processing tools (PCA and PLS models). The objective of the current paper is to collect and consistently present information on various aspects of FT-IR spectroscopy, which is not only a well-known and well-established method but is also continuously developing. The innovative aspect of the current review is to show FT-IR's great versatility that allows its applications to solve and explain issues from both the scientific domain (e.g., hydrogen bonds) and practical ones (e.g., technological processes, medicine, environmental protection, and food analysis). Particular attention was paid to the issue of hydrogen bonds as key non-covalent interactions, conditioning the existence of living matter and determining the number of physicochemical properties of various materials. Since the role of FT-IR spectroscopy in the field of hydrogen bond research has great significance, a historical outline of the most important qualitative and quantitative hydrogen bond theories is provided. In addition, research on selected unconventional spectral effects resulting from the substitution of protons with deuterons in hydrogen bridges is presented. The state-of-the-art and originality of the current review are that it presents a combination of uses of FT-IR spectroscopy to explain the way molecules vibrate and the effects of those vibrations on macroscopic properties, hence practical applications of given substances.

Mechanism of improving emulsion stability of emulsion-type sausage with oyster mushroom (Pleurotus ostreatus) powder as a phosphate replacement

This research evaluated the potentiality of oyster mushroom powder (OMP) as a phosphate alternative by improving emulsion stability of emulsion-type sausage. Sausage without phosphate (NC), with 0.2% sodium triphosphate (PC), and with 1 and 2% OMP (M1 and M2) were prepared. The OMP addition improved the physicochemical properties of sausage, effectively prevented lipid oxidation, and delayed the growth of aerobic bacteria during 28 days of cold storage compared to NC. The M1 and M2 improved the emulsion stability similar to PC. M2 had the highest water holding capacity and apparent viscosity and the lowest cooking loss (P < 0.05). The addition of OMP resulted in different textural characteristics from that of phosphate due to the formation of emulsion structures randomly entrapped by filament-like components, which were derived from polysaccharides or the conjugates between polysaccharides and proteins. According to the results of this study, emulsion stability promoted by OMP was mainly due to the polysaccharides, which are involved in enhancing viscosity and steric hindrance.

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares–Regression (PLS-R) and support vector machine–regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error—RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: “satisfactory”: 4−7 log CFU/g, “acceptable”: 7−8 log CFU/g, and “unacceptable”: >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

Real-Time Monitoring the Effects of Storage Conditions on Volatile Compounds and Quality Indexes of Halal-Certified Kimchi during Distribution Using Electronic Nose

The food logistics system is an essential sector for maintaining and monitoring the safety and quality of food products and becoming more crucial, especially during and after the pandemic of COVID-19. Kimchi is a popular traditional fermented food originally from Korea and easily changes because of the storage conditions. This study aims to evaluate the effects and the contributions of temperature to volatile compounds, quality indexes, and the shelf life of Halal-certified Kimchi, and to identify alcohol and find the correlation between the identified variables using an electronic nose and conventional method with the integration of multivariate analysis. Thirty-two volatile compounds (VOCs) were detected and correlated with pH, titratable acidity (TA), and lactic acid bacteria (LAB) counts during storage time. Ethanol was also found in the ripened Kimchi and possibly became the critical point of halal Kimchi products besides total acidity, pH, and LAB. Furthermore, the correlation between pH and benzaldehyde, titratable acidity and 3-methylbutanoic acid, and among lactic acid bacteria with ethanol, acetic acid, ethyl acetate, and 3-methylbutanoic acid properly can be used as a given set of variables in the prediction of food quality during storage and distribution.

Effect of transglutaminase concentration in curing solution on the physicochemical properties of salted large yellow croaker (Pseudosciaena crocea)

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Quality of fish meat was improved by adding TGase in curing solution.

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Meat hardness of large yellow croaker salted with 1.0% TGase was the highest.

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Cross-linking of fish meat protein was promoted by TGase in curing solution.

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Concentration of TGase in curing solution affects its penetration during salting.

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Meat hardness of roasted fish was improved by pre-salting with TGase.

This study investigated the effect of transglutaminase (TGase) added to curing solution on the physicochemical properties of salted fish. Large yellow croaker was salted in the curing solution containing 0–2.0% TGase at 10 °C for 48 h. The hardness, moisture content and immobilized water ratio of fish salted with 1.0% TGase were 629.94 g, 59.14%, and 95.34% respectively, which decreased with increasing or decreasing TGase concentration. The scanning electron microscopy image showed that a compact structure on the meat surface of fish salted containing 1.0% TGase. A similar microstructure was found in the internal meat of fish salted with 0.5% TGase. The hardness of fish salted with 0.5% TGase after roasting was 1135.97 g, which was higher than that of fish salted without TGase. In conclusion, high-quality salted large yellow croaker can be obtained by adding TGase in curing solution.

Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy

As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.

Biomolecules, Fatty Acids, Meat Quality, and Growth Performance of Slow-Growing Chickens in an Organic Raising System

This study was to determine the effect of the organic raising system (OR) on growth performance, meat quality, and physicochemical properties of slow-growing chickens. Three hundred and sixty (one-day-old) Korat chickens (KRC) were randomly assigned to control (CO) and OR groups. The groups comprised six replicates of thirty chickens each. The chickens were housed in indoor pens (5 birds/m2), wherein those in OR had free access to Ruzi pasture (1 bird/4 m2) from d 21 to d 84 of age. In the CO group, chickens were fed with a mixed feed derived from commercial feedstuffs, while those in the OR group were fed with mixed feed derived from organic feedstuffs. The results revealed a lower feed intake (p < 0.0001) and feed conversion ratio (p = 0.004) in the OR. The OR increased total collagen, protein, shear force, color of skin and meat, and decreased abdominal fat (p < 0.05). The OR improved fatty acid with increased DHA, n-3 PUFA, and decreased the ratio of n-6 to n-3 PUFA in KRC meat (p < 0.05). The synchrotron radiation-based Fourier transform infrared spectroscopy and correlation loading analyses confirmed these results. In conclusion, our results proved that OR could improve growth performance and meat quality and suggested the raising system be adopted commercially. In addition, the observed differences in biochemical molecules could also serve as markers for monitoring meat quality.

Effects of β-alanine and L-histidine supplementation on carnosine contents in and quality and secondary structure of proteins in slow-growing Korat chicken meat

Carnosine enrichment of slow-growing Korat chicken (KRC) meat helps differentiate KRC from mainstream chicken. We aimed to investigate the effects of β-alanine and L-histidine supplementation on the carnosine synthesis in and quality and secondary structure of proteins in slow-growing KRC meat. Four hundred 21-day-old female KRC were used, and a completely randomized design was applied. The chickens were divided into 4 experimental groups: basal diet (A), basal diet supplemented with 1.0% β-alanine (B), 0.5% L-histidine (C), and 1.0% β-alanine combined with 0.5% L-histidine (D). Each group consisted of 5 replicates (20 chickens per replicate). On d 70, 2 chickens per replicate were slaughtered, and the levels of carnosine, anserine, and thiobarbituric acid reactive substances were analyzed. Biochemical changes were monitored using synchrotron radiation-based Fourier transform infrared microspectroscopy; 5 chickens per replicate were slaughtered, and the meat quality was analyzed. Statistical analysis was performed using ANOVA and principal component analysis (PCA). Group D chickens exhibited the highest carnosine meat content, followed by those in groups B and C. However, amino acid supplementation did not affect anserine content and growth performance. Higher carnosine levels correlated with increasing pH_{45 min} and decreasing drip loss, cooking loss, shear force, and lipid oxidation. PCA revealed that supplementation with only β-alanine or L-histidine was related to increased content of β-sheets, β-turns, and aliphatic bending groups and decreased content of α-helix groups. This study is the first to report such findings in slow-growing chicken. Our findings suggest that KRC can synthesize the highest carnosine levels after both β-alanine and L-histidine supplementation. Higher carnosine contents do not adversely affect meat quality, improve meat texture, and alter the secondary structures of proteins. The molecular mechanism underlying carnosine synthesis in chickens needs further study to better understand and reveal markers that facilitate the development of nutrient selection programs.

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm². Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm², respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using Sensor Fusion

The continuous development of more accurate and selective bio- and chemo-sensors has led to a growing use of sensor arrays in different fields, such as health monitoring, cell culture analysis, bio-signals processing, or food quality tracking. The analysis and information extraction from the amount of data provided by these sensor arrays is possible based on Machine Learning techniques applied to sensor fusion. However, most of these computing solutions are implemented on costly and bulky computers, limiting its use in in-situ scenarios outside complex laboratory facilities. This work presents the application of machine learning techniques in food quality assessment using a single Field Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power consumption as well as low-size allow the application of the proposed solution even in space constrained places, as in food manufacturing chains. As an example, the proposed system is tested on an e-nose developed for beef classification and microbial population prediction.

Investigation and Rapid Discrimination of Food-Related Bacteria under Stress Treatments Using IR Microspectroscopy

Because the robust and rapid determination of spoilage microorganisms is becoming increasingly important in industry, the use of IR microspectroscopy, and the establishment of robust and versatile chemometric models for data processing and classification, is gaining importance. To further improve the chemometric models, bacterial stress responses were induced, to study the effect on the IR spectra and to improve the chemometric model. Thus, in this work, nine important food-relevant microorganisms were subjected to eight stress conditions, besides the regular culturing as a reference. Spectral changes compared to normal growth conditions without stressors were found in the spectral regions of 900-1500 cm^-1 and 1500-1700 cm^-1. These differences might stem from changes in the protein secondary structure, exopolymer production, and concentration of nucleic acids, lipids, and polysaccharides. As a result, a model for the discrimination of the studied microorganisms at the genus, species and strain level was established, with an accuracy of 96.6%. This was achieved despite the inclusion of various stress conditions and times after incubation of the bacteria. In addition, a model was developed for each individual microorganism, to separate each stress condition or regular treatment with 100% accuracy.

ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder

In a preliminary study, commercial insect powders were successfully identified using infrared spectroscopy combined with multivariate analysis. Nonetheless, it is necessary to check if this technology is capable of discriminating, predicting, and quantifying insect species once they are used as an ingredient in food products. The objective of this research was to study the potential of using attenuated total reflection Fourier transform mid-infrared spectroscopy (ATR-FTMIR) combined with multivariate analysis to discriminate doughs and 3D-printed baked snacks, enriched with Alphitobius diaperinus and Locusta migratoria powders. Several doughs were made with a variable amount of insect powder (0–13.9%) replacing the same amount of chickpea flour (46–32%). The spectral data were analyzed using soft independent modeling of class analogy (SIMCA) and partial least squares regression (PLSR) algorithms. SIMCA models successfully discriminated the insect species used to prepare the doughs and snacks. Discrimination was mainly associated with lipids, proteins, and chitin. PLSR models predicted the percentage of insect powder added to the dough and the snacks, with determination coefficients of 0.972, 0.979, and 0.994 and a standard error of prediction of 1.24, 1.08, and 1.90%, respectively. ATR-FTMIR combined with multivariate analysis has a high potential as a new tool in insect product authentication.

Detection of Meat Adulteration Using Spectroscopy-Based Sensors

Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.

The significant influence of residual feed intake on flavor precursors and biomolecules in slow-growing Korat chicken meat

Objective

This study investigated the association between feed efficiency, physicochemical properties, flavor precursors and biomolecules in the thigh meat of Korat (KR) chickens.

Methods

The feed intake and body weight of individual male KR chickens were recorded from 1 to 10 weeks old to calculate the individual residual feed intake (RFI) of 75 birds. At 10 weeks of age, chickens with the 10 highest (HRFI) and lowest RFI (LRFI) were slaughtered to provide thigh meat samples. The physicochemical properties (ultimate pH, water holding capacity [WHC], drip loss) and flavor precursors (guanosine monophosphate, inosine monophosphate (IMP), adenosine monophosphate and inosine) were analyzed conventionally, and Fourier transform infrared spectroscopy was used to identify the composition of biomolecules (lipids, ester lipids, amide I, amide II, amide III, and carbohydrates) and the secondary structure of the proteins. A group t-test was used to determine significant differences between mean values and principal component analysis to classify thigh meat samples into LRFI and HRFI KR chickens.

Results

The physicochemical properties of thigh meat samples from LRFI and HRFI KR chickens were not significantly different but the IMP content, ratios of lipid, lipid ester, protein (amide I, amide II) were significantly different (p<0.05). The correlation loading results showed that the LRFI group was correlated with high ratios of lipids, lipid esters, collagen content (amide III) and beta sheet protein (rg loading >0.5) while the HRFI group was positively correlated with protein (amide I, amide II), alpha helix protein, IMP content, carbohydrate, ultimate pH and WHC (rg loading >0.5).

Conclusion

The thigh meat from chickens with different RFI differed in physiochemical properties affecting meat texture, and in the contents of flavor precursors and biomolecules affecting the nutritional value of meat. This information can help animal breeders to make genetic improvements by taking more account of traits related to RFI.