Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts

Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.

Liver glycogen fragility in the presence of hydrogen-bond breakers

Glycogen, a complex branched glucose polymer, is responsible for sugar storage in blood glucose homeostasis. It comprises small β particles bound together into composite α particles. In diabetic livers, α particles are fragile, breaking apart into smaller particles in dimethyl sulfoxide, DMSO; they are however stable in glycogen from healthy animals. We postulate that the bond between β particles in α particles involves hydrogen bonding. Liver-glycogen fragility in normal and db/db mice (an animal model for diabetes) is compared using various hydrogen-bond breakers (DMSO, guanidine and urea) at different temperatures. The results showed different degrees of α-particle disruption. Disrupted glycogen showed changes in the mid-infra-red spectrum that are related to hydrogen bonds. While glycogen α-particles are only fragile under harsh, non-physiological conditions, these results nevertheless imply that the bonding between β particles in α particles is different in diabetic livers compared to healthy, and is probably associated with hydrogen bonding.

The ability of a portable near infrared instrument to evaluate the shelf-life of fresh and thawed goat muscles

The objective of this study was to evaluate the use of a portable near infrared (NIR) instrument to monitor the shelf-life of four goat muscles [longissimus thoracis et lumborum (LTL), semimembranosus (SM), semitendinosus (ST) and biceps femoris (BF)] stored for up to 8 days (4 °C). The NIR spectra of the muscle samples were collected at day 0, and after 1, 4 and 8 days of storage using a MicroNIR instrument (900-1600 nm). The coefficient of determination in cross-validation (R2) and the standard error in cross validation (SECV) obtained for the prediction of days of storage ranged between 0.76 and 0.86, where the SECV ranged from 0.32 to 0.41. The best statistics in cross-validation were obtained for the prediction of days of storage in the BF samples, followed by the ST and LTL muscles. Differences in the PLS loadings for the cross-validation models were observed due to the interactions between the different muscle samples and days of storage. Overall, these results showed the potential of NIR spectroscopy to identify the time of storage in four different goat muscles. Similar data and techniques could be used to predict the remaining shelf life of meat derived from different species under storage. This information can then be used as a tool to predict and guarantee the safety of meat samples to the consumer along the meat supply and value chains.

Rapid screening of bacteriostatic and bactericidal antimicrobial agents against Escherichia coli by combining machine learning (artificial intelligence) and UV-VIS spectroscopy

Antibiotics are compounds that have a particular mode of action upon the microorganism they are targeting. However, discovering and developing new antibiotics is a challenging and timely process. Antibiotic development process can take up to 10-15 years and over $1billion to develop a single new therapeutic product. Rapid screening tools to understand the mode of action of the new antimicrobial agent are considered one of the main bottle necks in the antimicrobial agent development process. Classical approaches require multifarious microbiological methods and they do not capture important biochemical and organism therapeutic-interaction mechanisms. This work aims to provide a rapid antibiotic-antimicrobial biochemical diagnostic tool to reduce the timeframes of therapeutic development, while also generating new biochemical insight into an antimicrobial-therapeutic screening assay in a complex matrix. The work evaluates the effect of antimicrobial action through "traditional" microbiological analysis techniques with a high-throughput rapid analysis method using UV-VIS spectroscopy and chemometrics. Bacteriostatic activity from tetracycline and bactericidal activity from amoxicillin were evaluated on a system using non-resistant Escherichia coli O157:H7 by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), and UV-VIS spectroscopy (high-throughput analysis). The data were analysed using principal component analysis (PCA) and support vector machine (SVM) classification. The rapid diagnostic technique could easily identify differences between bacteriostatic and bactericidal mechanisms and was considerably quicker than the "traditional" methods tested.

Finding biomarkers of experience in animals

At a time when there is a growing public interest in animal welfare, it is critical to have objective means to assess the way that an animal experiences a situation. Objectivity is critical to ensure appropriate animal welfare outcomes. Existing behavioural, physiological, and neurobiological indicators that are used to assess animal welfare can verify the absence of extremely negative outcomes. But welfare is more than an absence of negative outcomes and an appropriate indicator should reflect the full spectrum of experience of an animal, from negative to positive. In this review, we draw from the knowledge of human biomedical science to propose a list of candidate biological markers (biomarkers) that should reflect the experiential state of non-human animals. The proposed biomarkers can be classified on their main function as endocrine, oxidative stress, non-coding molecular, and thermobiological markers. We also discuss practical challenges that must be addressed before any of these biomarkers can become useful to assess the experience of an animal in real-life.

Near-infrared spectroscopy (NIRS) for monitoring the nutritional composition of black soldier fly larvae (BSFL) and frass

BACKGROUND

The demand for protein obtained from animal sources is growing rapidly, as is the necessity for sustainable animal feeds. The use of black soldier fly larvae (BSFL) reared on organic side streams as sustainable animal feed has been receiving attention lately. This study assessed the ability of near-infrared spectroscopy (NIRS) combined with chemometrics to evaluate the nutritional profile of BSFL instars (fifth and sixth) and frass obtained from two different diets, namely soy waste and customised bread-vegetable diet. Partial least squares (PLS) regression with leave one out cross-validation was used to develop models between the NIR spectral data and the reference analytical methods.

RESULTS

Calibration models with good [coefficient of determination in calibration (Rcal 2 ): 0.90; ratio of performance to deviation (RPD) value: 3.6] and moderate (Rcal 2 : 0.76; RPD value: 2.1) prediction accuracy was observed for acid detergent fibre (ADF) and total carbon (TC), respectively. However, calibration models with moderate accuracy were observed for the prediction of crude protein (CP) (Rcal 2 : 0.63; RPD value: 1.4), crude fat (CF) (Rcal 2 : 0.70; RPD value: 1.6), neutral detergent fibre (NDF) (Rcal 2 : 0.60; RPD value: 1.6), starch (Rcal 2 : 0.52; RPD value: 1.4), and sugars (Rcal 2 : 0.52; RPD value: 1.4) owing to the narrow or uneven distribution of data over the range evaluated.

CONCLUSION

The near-infrared (NIR) calibration models showed a good to moderate prediction accuracy for the prediction of ADF and TC content for two different BSFL instars and frass reared on two different diets. However, calibration models developed for predicting CP, CF, starch, sugars and NDF resulted in models with limited prediction accuracy. © 2023 Society of Chemical Industry.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Near infrared spectroscopy combined with chemometrics as tool to monitor starch hydrolysis

The objective of this study was to evaluate the feasibility of using near infrared (NIR) spectroscopy combined with principal component analysis (PCA) and partial least squares (PLS) regression to monitor the in vitro hydrolysis of different starch substrates. Potato and rice starches, and pre-gelatinised corn starch were used, where samples collected at different time points (5 to 120 min) during the in vitro hydrolysis and analysed using a Fourier transform NIR instrument with a gold-coated integrating sphere (diffuse reflection). PLS regression models between the spectra and reference data yield a coefficient of determination in cross validation (R2CV) and standard error in cross validation (SECV) of 0.94 and 1105. 8 μg mL-1; 0.81 and 440.81 μg mL-1; 0.45 and 338 μg mL-1; 0.70 and 276 μg mL-1; 0.75 and 296. 2 μg mL-1 for the prediction of the concentration of maltose using all samples, rice and potato combined, and pre-gelatinised corn, potato and rice starches analysed separately, respectively. It was concluded that the combination of NIR spectroscopy with both PCA and PLS regression might provide with a rapid and efficient tool to rapidly monitor changes that occur during the in vitro hydrolysis of starch.

Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System

Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R2CV) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R2CV of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R2CV of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R2CV of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness.

Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety

Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.

Phenolics and Spectroscopy: Challenges and Successful Stories in The Grape and Wine Industry

BACKGROUND

Phenolic compounds are considered to have a major role in the quality of grapes and wine. These compounds contribute to the sensory perception of red wine as they are involved in astringency and bitterness as well as in determining the colour intensity of grapes and wine (e.g. anthocyanins content). Several techniques are used to characterise and quantify these compounds in grapes and wine samples such as UV-Visible spectroscopy or high-performance liquid chromatography (HPLC). More recently, different applications and reports have shown the value of vibrational spectroscopy techniques to monitor and measure phenolic compounds along the grape and wine value chain.

RESULTS

This article summarises as well as discusses challenges and successful stories in relation to the utilization of vibrational spectroscopy techniques to measure phenolic compounds in grapes and wine. Specifically, content presented at the workshop "Outstanding sensors challenge beverage and food future" organised by Italian Society of Food Science and Technology and the University of Pisa (Pisa, Italy) is summarised.

CONCLUSION

Although vibrational spectroscopy techniques have been proven to be of importance to measure composition across the grape and wine value chain, the adoption of these technologies has been compromised by the accessibility and price of instruments. Understanding the basic principles of the different vibrational spectroscopy methods (e.g. characteristics, limit of detection) as well as how to effectively use the data generated are still main barriers facing the incorporation of these techniques into the grape and wine industry. Furthermore, is still not clear for many users of these technologies how they will contribute to the sustainability of the wine industry as well as to preserve the identity of the wine making process. This article is protected by copyright. All rights reserved.

The Use of a Droplet Collar Accessory Attached to a Portable near Infrared Instrument to Identify Methanol Contamination in Whisky

The aim of this study was to evaluate the ability of a droplet collar accessory attached to a portable near-infrared (NIR) instrument to characterize the artificial contamination of methanol in commercial whisky samples. Unadulterated samples (n = 12) were purchased from local bottle shops where adulterated samples were created by adding methanol (99% pure methanol) at six levels (0.5%, 1%, 2%, 3%, 4% and 5% v/v) to the commercial whisky samples (controls). Samples were analyzed using a drop collar accessory attached to a MicroNIR Onsite instrument (900-1650 nm). Partial least squares (PLS) cross-validation statistics obtained for the prediction of all levels of methanol (from 0 to 5%) addition were considered adequate when the whole adulteration range was used, coefficient of determination in cross-validation (R2cv: 0.95) and standard error in cross of validation (SECV: 0.35% v/v). The cross-validation statistics were R2cv: 0.97, SECV: 0.28% v/v after the 0.5% and 1% v/v methanol addition was removed. These results showed the ability of using a new sample presentation attachment to a portable NIR instrument to analyze the adulteration of whisky with methanol. However, the low levels of methanol adulteration (0.5 and 1%) were not well predicted using the NIR method evaluated.

Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.

Discrimination of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) binary mixtures using a portable near infrared instrument combined with chemometrics

Consumers demand safe and nutritious foods at accessible prices; where issues associated with adulteration, fraud, and provenance have become important aspects to be considered by the modern food industry. There are many analytical techniques and methods available to determine food composition and quality, including food security. Among them, vibrational spectroscopy techniques are at the first line of defence (near and mid infrared spectroscopy, and Raman spectroscopy). In this study, a portable near infrared (NIR) instrument was evaluated to identify different levels of adulteration between binary mixtures of exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) sourced from a commercial abattoir were used to make different binary mixtures (95 % %w/w, 90 % %w/w, 50 % %w/w, 10 % %w/w and 5 % %w/w) and analysed using a portable NIR instrument. The NIR spectra of the meat mixtures was analysed using principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Two isosbestic points corresponding to absorbances at 1028 nm and 1224 nm were found to be consistent across all the binary mixtures analysed. The coefficient of determination in cross validation (R²) obtained for the determination of the per cent of species in a binary mixture was above 90 % with a standard error in cross validation (SECV) ranging between 12.6 and 15 %w/w. Overall, the results of this study indicate that NIR spectroscopy can determine the level or ratio of adulteration in the binary mixtures of minced meat.

Pre-processing Applied to Instrumental Data in Analytical Chemistry: A Brief Review of the Methods and Examples

The field of analytical chemistry has been significantly advanced by the availability of state-of-the-art instrumentation, allowing for the development of novel applications in this field. However, in many cases, the direct interpretation of the recorded data is often not straightforward, hence some level of pre-processing is required (e.g., baseline correction, derivatives, normalization, smoothing). These techniques have become a critical first step for the successful analysis of the data recorded, and it is recommended to use them before the application of chemometrics (e.g., classification, calibration development). The aim of this paper is to provide with an overview of the most used pre-processing methods applied to instrumental analytical methods (e.g., spectroscopy, chromatography). Examples of their application in near infrared and UV-VIS spectroscopy as well as in gas chromatography will be also discussed. Overall, this paper provides with a comprehensive understanding of pre-processing techniques in analytical chemistry, highlighting their importance during the analysis and interpretation of data, as well as during the development of accurate and reliable chemometric models.

Determination of glucosinolates and isothiocyanates in glucosinolate-rich vegetables and oilseeds using infrared spectroscopy: A systematic review

Cruciferous vegetables and oilseeds are rich in glucosinolates that can transform into isothiocyanates upon enzymic hydrolysis during post-harvest handling, food preparation and/or digestion. Vegetables contain glucosinolates that have beneficial bioactivities, while glucosinolates in oilseeds might have anti-nutritional properties. It is therefore important to monitor and assess glucosinolates and isothiocyanates content through the food value chain as well as for optimized crop production. Vibrational spectroscopy methods, such as infrared (IR) spectroscopy, are used as a nondestructive, rapid and low-cost alternative to the current and common costly, destructive, and time-consuming techniques. This systematic review discusses and evaluates the recent literature available on the use of IR spectroscopy to determine glucosinolates and isothiocyanates in vegetables and oilseeds. NIR spectroscopy was used to predict glucosinolates in broccoli, kale, rocket, cabbage, Brussels sprouts, brown mustard, rapeseed, pennycress, and a combination of Brassicaceae family seeds. Only one study reported the use of NIR spectroscopy to predict broccoli isothiocyanates. The major limitations of these studies were the absence of the critical evaluation of errors associated with the reference method used to develop the calibration models and the lack of interpretation of loadings or regression coefficients used to predict glucosinolates.

Emerging nondestructive techniques to quantify the textural properties of food: A state-of-art review

Texture is an important sensory attribute that drives consumer acceptance of any food material. In recent times consumers' demand for high-quality food urges food industries to provide food with consistent textural properties. However, texture measurement not just requires a trained sensory panel but also a considerable amount of time and effort. On the flip side, human observation could be subjective hence repeatability of the result may not be ensured and/or relied on. Contrary to that, objective methods for texture measurement are reliable and consistent, but are not suitable for in-line application and also destructive in nature. The mentioned crisis has made industries opt for nondestructive texture analysis techniques. In the past decade, considerable research has been carried out on nondestructive texture analysis methods such as micro-deformation, and acoustic and optical techniques, showing feasibility for in-line applications. The current review focuses on the working principles and most recent applications of nondestructive techniques for texture analysis of food products. Moreover, a detailed review of contact and noncontact-type texture measurement has been presented in this article. The literature survey is concluded with future research aspects and challenges involved in the commercialization of the nondestructive texture analysis techniques.

Advances in Spectrometric Techniques in Food Analysis and Authentication

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Mid-infrared spectroscopy for the rapid quantification of eucalyptus oil adulteration in Australian tea tree oil (Melaleuca alternifolia)

Essential oil distilled from Melaleuca alternifolialeaves, commonly known as tea tree oil, is well known for its biological activity, principally its antimicrobial properties. However, many samples are adulterated with other, cheaper essential oils such as eucalyptus oil. Current methods of detecting such adulteration are costly and time-consuming, making them unsuitable for rapid authentication screening. This study investigated the use of mid-infrared (MIR) spectroscopy for detecting and quantifying the level of eucalyptus oil adulteration in spiked samples of pure Australian tea tree oil. To confirm the authenticity of the tea tree oil samples, GC-MS analysis was used to profile 37 of the main volatile constituents present, demonstrating that the samples conformed to ISO specifications. Three chemometric regression techniques (PLSR, PCR and SVR) were trialled on the MIR spectra, along with a variety of pre-processing techniques. The best-performing full-wavelength PLSR model showed excellent prediction of eucalyptus oil content, with an R²_CV of 0.999 and RMSECV of 1.08 % v/v. The RMSECV could be further improved to 0.82 % v/v through a moving window wavenumber optimisation process. The results suggest that MIR spectroscopy combined with PLSR can be used to predict eucalyptus oil adulteration in Australian tea tree oil samples with a high level of accuracy.

Feasibility of near infrared spectroscopy for estimating suppressiveness of carnation (Dianthus cariophyllus L.) fusarium wilt in different plant growth media

Fusarium wilt is one of the most widespread diseases in carnation crops in a large number of countries. Plant protection products commonly used to remedy the disease have been considered ineffective and environmentally unsafe for commercial use. As an alternative, the use of suppressive growth media has been proposed. The aim of this study was to evaluate the use of a rapid method such as near infrared spectroscopy (NIRS) to monitor and evaluate suppressive media potential. The NIR spectra were collected from 6 plant growth media used in a series of trials to evaluate suppressiveness to carnation Fusarium wilt namely grape marc compost, cork compost, olive oil husk + cotton gin trash composted and mixed with rice husk, spent mushroom composted and mixed with peat, coir fiber and light peat. The NIR calibration models showed promising results for estimating pH, β-glucosidase activity, disease severity (AUDPC and RLSBX) in the growth media evaluated, with coefficients of determination of 0.99, 0.98, 0.98 and 0.90; SECV of 0.09, 11.63, 0.05 and 0.10; and RPD values of 13.86, 6.62, 7.19 and 3.24; respectively. NIR spectroscopy could become a useful non-destructive and fast analytical tool for the identification of Fusarium wilt suppressive composts, avoiding the use of reagents.

Holistic approach to effects of foods, human physiology, and psychology on food intake and appetite (satiation & satiety)

Appetite (satiation and satiety) is an essential element for the control of eating behavior, and as a consequence human nutrition, body weight, and chronic disease risk. A better understanding of appetite mechanisms is necessary to modulate eating behavior and food intake, and also provide a practical approach for weight management. Although many researchers have investigated the relationships between satiation/satiety and specific factors including human physiology, psychology, and food characteristics, limited information on the interactions between factors or comparisons between the relative importance of factors in contributing to satiation/satiety have been reported. This article reviews progress and gaps in understanding individual attributes contributing to perceived satiation/satiety, the advantages of considering multiple factors together in appetite experiments, as well as the applications of nondestructive sensing in evaluating human factors contributing to relative appetite perception. The approaches proposed position characterization of appetite (satiation and satiety) for personalized and precision nutrition in relation to human status and healthy diets. In particular, it is recommended that future studies of appetite perception recognize the inter-dependence of food type and intake, appetite (satiation and satiety), and individual status.

Characterisation and Identification of Individual Intact Goat Muscle Samples (Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics

Adulterated, poor-quality, and unsafe foods, including meat, are still major issues for both the food industry and consumers, which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near-infrared (NIR) instrument, combined with chemometrics, to identify and classify individual-intact fresh goat muscle samples. Fresh goat carcasses (n = 35; 19 to 21.7 Kg LW) from different animals (age, breeds, sex) were used and separated into different commercial cuts. Thus, the longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900–1600 nm) using a portable NIR instrument. Differences in the NIR spectra of the muscles were observed at wavelengths of around 976 nm, 1180 nm, and 1430 nm, associated with water and fat content (e.g., intramuscular fat). The classification of individual muscle samples was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68–94%) using the second-derivative NIR spectra. The results indicated that NIR spectroscopy could be used to identify individual goat muscles.

Influence of blanching on the bioactive compounds of Terminalia ferdinandiana Exell fruit during storage

Changes in quality of fruits and vegetables during processing and storage might impact on the nutritional and economical value of food products. The present study aimed to evaluate the influence of blanching on the content of vitamin C and ellagic acid (EA) as the main bioactive compounds present in Kakadu plum (KP) fruits (Terminalia ferdinandiana) during storage at 40oC to mimic typical temperature when wild harvested. Changes in the profile of fatty acids, malondialdehyde (MDA) production, as a biomarker for lipid peroxidation, and antioxidant properties of KP fruits were evaluated. The results revealed that vitamin C decreased between 25 and 52% over the storage period. Statistically significant differences in the concentration of vitamin C were associated with temperature (p < 0.05) and blanching (p < 0.05), whereas no significant differences in EA during storage were observed. DPPH radical scavenging capacity and total phenolic content of both blanched and control samples decreased by 80% and 35%, respectively, at the end of the storage period compared to day 0. In addition, the change in DPPH activity is significantly correlated (Pearson R2 = 0.829, p ˂ 0.01) with the breakdown of ellagitannins. Furthermore, KP fruit demonstrated excellent antioxidative properties by reducing MDA production. It was concluded that blanching causes significant vitamin C loss whereas neither blanching nor long-term storage at elevated temperature affect the EA content. The results also indicate that the antioxidant compounds present in the KP fruits provided considerable protection against fatty acid oxidation during storage.

Near-infrared spectroscopy as a feasible method for the differentiation of Neisseria gonorrhoeae from Neisseria commensals and antimicrobial resistant from susceptible gonococcal strains

Rapid and cost-effective diagnosis of Neisseria gonorrhoeae (NG) are important measures for the control and management of gonococcal infection. Current diagnostic tools such as nucleic acid amplification tests and bacterial culture are not feasible in many resource-poor settings, and so syndromic patient management is commonplace. Alternative cost-effective diagnostic tools are therefore needed. Here, we sought to explore the utility and feasibility of Near Infrared Spectroscopy (NIRS) to (1) identify and differentiate NG from Neisseria commensals and (2) to differentiate fully susceptible NG from resistant NG. NIRS correctly classified NG from Neisseria commensals (R²= 0.89; SECV 0.164) and to a lesser capacity, susceptible NG from resistant (R² = 0.60; SECV 0.32). To the best our knowledge, this is the first proof of concept study in the field. Further evaluations are now warranted to enhance capacity and accuracy of this diagnostic approach.

Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy

Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.

Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.)

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification.

Integrating Effects of Human Physiology, Psychology, and Individual Variations on Satiety–An Exploratory Study

Satiety can influence food intake, and as a consequence has the potential to affect weight and obesity. Human factors such as physiology and psychology are likely to be important in determining satiety. However, it is not well-understood how these factors (individual variations) alone or combined contribute to satiety feelings. In addition, there have been limited or no attempts to use a holistic approach to evaluate satiety. In this study, three plant-based foods were used as mid-morning snack for 52 participants to evaluate satiety response (during three consecutive days, one-day-one-food type). The foods were served ad libitum until participants felt comfortably full prior to satiety monitoring. The study explored diverse human factors (n = 30) that might contribute to satiety including those related to oral physiology, metabolic factors, body composition and psychology. It identified important variables for satiety as well as the interactions among them and the influences of age, gender, and low satiety phenotype (consistently lower reported fullness scores) on satiety. Overall, combinations of factors rather than individual ones contributed to self-reported satiety. Food factors (e.g., type, composition) had limited effects, but there were only three types used in the study. The combination of metabolic factors [respiratory quotient, age, and body energy usage type (e.g., carbohydrate or fat)], oral sensitivity & processing, personality traits (agreeableness, conscientiousness, and neuroticism), and eating behavior (e.g., emotional and external eating) were the most important for explaining individual satiety responses. Older participants had significantly higher reported satiety than younger participants, associated with significant differences in oral physiology, increased body fat, and mature psychological characters. Moreover, different satiety phenotypes had significant differences in relationships with body fat, oral physiology, personalities, food neophobia, and eating behaviors. The results of this study indicate that much greater insights into the factors determining satiety responses can be obtained by combining multiple food and human physiological and psychological characteristics. This study used more diverse measures of individual variation than previous studies of satiety and points the way toward a more holistic approach to understanding the (control of) perceptions of fullness at both individual and group levels.

Application of near-infrared spectroscopy/artificial neural network to quantify glycosylated norisoprenoids in Tannat grapes

Grape variety, vinification, and ageing are factors conditioning the aroma of a wine, with volatile secondary metabolites responsible for the so-called grape varietal character. Particularly, grape glycosylated norisoprenoids are mostly responsible for the sensory profile of Tannat wines, making relevant the use of fast instrumental tools to evaluate their concentration, allow classifying grapes and defining the optimum maturity for harvest. NIR spectroscopy is a fast, non-destructive technique, which requires minimal sample preparation. However, its quantitative applications need chemometric models for interpretation. In this work, a NIR-ANN calibration was developed to quantify norisoprenoids in Vitis vinifera cv. Tannat grapes during maturation and harvesting. Glycosidated norisoprenoids were determined by GC-MS. The ANN adjustments showed better performance than linear models such as PLS, while the best calibration was obtained by homogenising grape samples when comparing to grape juice; making possible to fit a model with an error of 146 μg/kg.

Predictors of in-hospital mortality of COVID-19 patients and the role of telemetry in an internal medicine ward during the third phase of the pandemic

OBJECTIVE

The first pandemic phase of COVID-19 in Italy was characterized by high in-hospital mortality ranging from 23% to 38%. During the third pandemic phase there has been an improvement in the management and treatment of COVID-19, so mortality and predictors may have changed. A prospective study was planned to identify predictors of mortality during the third pandemic phase.

PATIENTS AND METHODS

From 15 December 2020 to 15 May 2021, 208 patients were hospitalized (median age: 64 years; males: 58.6%); 83% had a median of 2 (IQR,1-4) comorbidities; pneumonia was present in 89.8%. Patients were monitored remotely for respiratory function and ECG trace for 24 hours/day. Management and treatment were done following the timing and dosage recommended by international guidelines.

RESULTS

79.2% of patients necessitated O2-therapy. ARDS was present in 46.1% of patients and 45.4% received non-invasive ventilation and 11.1% required ICU treatment. 38% developed arrhythmias which were identified early by telemetry and promptly treated. The in-hospital mortality rate was 10%. At multivariate analysis independent predictors of mortality were: older age (R-R for≥70 years: 5.44), number of comorbidities ≥3 (R-R 2.72), eGFR ≤60 ml/min (RR 2.91), high d-Dimer (R-R for≥1,000 ng/ml:7.53), and low PaO2/FiO2 (R-R for <200: 3.21).

CONCLUSIONS

Management and treatment adherence to recommendations, use of telemetry, and no overcrowding appear to reduce mortality. Advanced age, number of comorbidities, severe renal failure, high d-Dimer and low P/F remain predictors of poor outcome. The data help to identify current high-risk COVID-19 patients in whom management has yet to be optimized, who require the greatest therapeutic effort, and subjects in whom vaccination is mandatory.

An Overview of the Successful Application of Vibrational Spectroscopy Techniques to Quantify Nutraceuticals in Fruits and Plants

Vibrational spectroscopy techniques are the most used techniques in the routine analysis of foods. This technique is widely utilised to measure and monitor the proximate chemical composition (e.g., protein, dry matter, fat and fibre) in an array of agricultural commodities, food ingredients and products. Developments in optics, instrumentation and hardware concomitantly with data analytics, have allowed for the progress in novel applications of these technologies in the field of nutraceutical and bio compound analysis. In recent years, several studies have demonstrated the capability of vibrational spectroscopy to evaluate and/or measure these nutraceuticals in a broad selection of fruit and plants as alternative to classical analytical approaches. This article highlights, as well as discusses, the challenges and opportunities that define the successful application of vibrational spectroscopy techniques, and the advantages that these techniques have to offer to evaluate and quantify nutraceuticals in fruits and plants.

A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism

The objective of this study was to evaluate the ability of a portable near infrared (NIR) instrument to analyse different tissues in healthy individuals in vivo and relate the spectral data with age, resting metabolic rate (RMR), respiratory quotient (RQ), body fat (BF) and body mass index (BMI). A NIR (950-1600 nm) instrument was used to collect the spectra of different tissues and partial least squares (PLS) regression was used to relate the NIR with RMR, RQ, BF and BMI. Results shown that non-destructive techniques such as those based in vibrational spectroscopy have potential to be used as tools to better categorise and understand the complex inter-individual differences that determine interactions between physiology and nutrition.

A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics

Humans are exposed to an ever-increasing number of environmental toxicants, some of which have gradually been elucidated to be important risk factors for metabolic diseases, such as diabetes and obesity. These metabolism-sensitive diseases typically occur when key metabolic and signaling pathways were disrupted, which can be influenced by the exposure to contaminants such as endocrine disrupting chemicals (EDCs), along with genetic and lifestyle factors. This promotes the concept and research on environmental metabolism disrupting chemicals (MDCs). In addition, identifying endogenous biochemical markers of effect linked to disease states is becoming an important tool to screen the biological targets following environmental contaminant exposure, as well as to provide an overview of toxicity risk assessment. As such, the current review aims to contribute to the further understanding of exposome and human health and disease by characterizing environmental exposure and effect metabolic biomarkers. We summarized MDC-associated metabolic biomarkers in laboratory animal and human cohort studies using high throughput targeted and nontargeted metabolomics techniques. Contaminants including heavy metals and organohalogen compounds, especially EDCs, have been repetitively associated with metabolic disorders, whereas emerging contaminants such as perfluoroalkyl substances and microplastics have also been found to disrupt metabolism. In addition, we found major limitations in the effective identification of metabolic biomarkers especially in human studies, toxicological research on the mixed effect of environmental exposure has also been insufficient compared to the research on single chemicals. Thus, it is timely to call for research efforts dedicated to the study of combined effect and metabolic alterations for the better assessment of exposomic toxicology and health risks. Moreover, advanced computational and prediction tools, further validation of metabolic biomarkers, as well as systematic and integrative investigations are also needed in order to reliably identify novel biomarkers and elucidate toxicity mechanisms, and to further utilize exposome and metabolome profiling in public health and safety management.

Nanozyme-based sensors for detection of food biomarkers: a review

Nanozymes have piqued the curiosity of scientists in recent years because of their ability to demonstrate enzyme-like activity combined with advantages such as high stability, inexpensive availability, robust activity, and tunable properties. These attributes have allowed the successful application of nanozymes in sensing to detect various chemical and biological target analytes, overcoming the shortcomings of conventional detection techniques. In this review, we discuss recent developments of nanozyme-based sensors to detect biomarkers associated with food quality and safety. First, we present a brief introduction to this topic, followed by discussing the different types of sensors used in food biomarker detection. We then highlight recent studies on nanozyme-based sensors to detect food markers such as toxins, pathogens, antibiotics, growth hormones, metal ions, additives, small molecules, and drug residues. In the subsequent section, we discuss the challenges and possible solutions towards the development of nanozyme-based sensors for application in the food industry. Finally, we conclude the review by discussing future perspectives of this field towards successful detection and monitoring of food analytes.

We present a discussion on different types of sensors used in food biomarker detection and highlight recent studies on nanozyme-based sensors to detect markers like toxins, pathogens, antibiotics, growth hormones, metal ions, additives, small molecules, drug residues.

Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis

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The use of vibrational spectroscopy combined with data analytics is discussed.

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The measure of bioactive compounds metabolites in fruit samples is presented.

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Advantages and limitations of these techniques are discussed.

The last three decades have demonstrated the ability of combining data analytics (e.g. big data, machine learning) with modern analytical instrumental techniques such as vibrational spectroscopy (VIBSPEC) (e.g. NIR, Raman, MIR) and sensing technologies (e.g. electronic noses and tongues, colorimetric sensors) to analyse, measure and monitor a wide range of properties and samples. Developments in instrumentation, hardware and software have placed VIBSPEC as a useful tool to quantify several bioactive compounds and metabolites in a wide range of fruit and plant samples. With the incorporation of hand-held and portable instrumentation, these techniques have been valuable for the development of in-field and high throughput applications, opened new frontiers of analysis in fruits and plants. This review will present and discuss some of the current applications on the use of VIBSPEC techniques combined with data analytics on the measurement bioactive compounds and plant metabolites in different fruit samples.

Microplastic adulteration in homogenized fish and seafood - a mid-infrared and machine learning proof of concept

The objective of this study was to assess the ability of utilizing attenuated total reflection mid-infrared (ATR-MIR) spectroscopy in combination with machine learning techniques to classify the presence of different types of microplastics in artificially adulterated fish and seafood samples. Different polymers namely poly-vinyl chloride (PVC), polycarbonate (PC), polystyrene (PS), polypropylene (PP) and low (LDPE) and high-density polyethylene (HDPE) were mixed with homogenized fish and seafood samples. Homogenized samples were analyzed using MIR spectroscopy and classification models developed using machine learning algorithms such as partial least squares discriminant analysis (PLS-DA). The results of this study revealed that it was possible to identify between adulterated and non-adulterated samples as well as the different microplastic types added to the homogenized samples using ATR-MIR spectroscopy. This study confirmed the ability of combining machine learning methods with ATR-MIR spectroscopy to directly analyze microplastic adulteration in fleshy foods such as fish and seafood. This proof-of-concept study can be utilized and extended to monitor the presence of plastics either in a wide range of fleshy foods or along the entire food value chain.

What's in this drink? Classification and adulterant detection in Irish Whiskey samples using near infrared spectroscopy combined with chemometrics

BACKGROUND

Near-infrared (NIR) spectroscopy coupled with principal component analysis (PCA) and partial least squares (PLS) regression was used to analyse a series of different Irish Whiskey samples in order to define their spectral profile and to assess the capability of the NIR method to identify samples based on their origin and storage (e.g. distiller, method of maturation). The ability of NIR spectroscopy to quantify the level of potential chemical adulterants was also investigated. Samples were spiked with 0.1%, 0.5%, 1.0%, 1.5% and 2.0% v/v of each adulterant (e.g. methanol, ethyl acetate, etc.) prior to NIR analysis.

RESULTS

The results of this study demonstrated the capability of NIR spectroscopy combined with PLS regression to classify the whiskey samples and to determine the level of adulteration. Moreover, the potential of NIR coupled with chemometric analysis as a rapid, portable, and non-destructive screening tool for quality control, traceability, and food/beverage adulteration for customs and other regulatory agencies, to mitigate beverage fraud was illustrated.

CONCLUSION

Given the non-specificity of the NIR technique, these positive preliminary results indicated that this method of analysis has the potential to be applied to identify the level of adulteration in distilled spirits. The rapid nature of the technique and lack of consumables or sample preparation required allows for a far more time and cost-effective analysis per sample. © 2021 Society of Chemical Industry.

Effects of drying methods and maltodextrin on vitamin C and quality of Terminalia ferdinandiana fruit powder, an emerging Australian functional food ingredient

BACKGROUND

Terminalia ferdinandiana, common name Kakadu plum (KP), fruit is a valuable source of vitamin C, and its concentration can be used as a quality index of KP products, such as dried fruit powder. The present study investigated the effects of two drying methods (freeze-drying and oven-drying) and the addition of maltodextrin (0-25%) on vitamin C, Maillard products, and overall quality of KP fruit powder.

RESULTS

Freeze-drying was a better dehydration technique than oven-drying in retaining vitamin C, reducing the formation of non-enzymatic browning and oxidation products, and improving powder colour (P < 0.05). Non-enzymatic browning products (furfural and 5-hydroxymethyl furfural) were generated in the oven-dried samples as a function of heating and high water activity. Maltodextrin acted as a vitamin C stabilizer in protecting vitamin C from oxidation, and significantly improved the colour attributes of the final dry products. Incorporation of 10-15% maltodextrin could reduce the percentage loss of vitamin C from 8.1% to 3.4% and 18.9% to 11.4% (compared with the control) during freeze-drying and oven-drying, respectively. Scanning electron micrographs revealed differences in the microstructures of the KP powder processed by the two drying methods with different levels of maltodextrin. Multivariate data analysis (principal component analysis) showed separation between the oven-dried and freeze-dried samples, and also suggested that addition of maltodextrin of 7.5-10% and 10-15% are effective for preserving vitamin C and other quality properties of the freeze- and oven-dried KP powder samples, respectively.

CONCLUSION

The results obtained are important for the KP industry, including Indigenous enterprises, in selecting the most appropriate drying method for KP fruit in terms of quality and sustainability. © 2021 Society of Chemical Industry.

From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy

Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.

Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum (Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

The use of vibrational spectroscopy to predict vitamin C in Kakadu plum powders (Terminalia ferdinandiana Exell, Combretaceae)

BACKGROUND

The objective of this study was to evaluate the feasibility of using either mid-infrared (MIR) or near-infrared (NIR) spectroscopy to predict the vitamin C content in Kakadu plum (Terminalia ferdinandiana Exell, Combretaceae) powder samples. Vitamin C is the main and quality-determining bioactive compound in Kakadu plum (KP). Kakadu plum powder samples were analyzed by ultra-performance liquid chromatography coupled to a photodiode array detector (UPLC-PDA) and scanned using both MIR and NIR spectroscopy.

RESULTS

The coefficient of determination (R² ) and the standard error in cross validation (SECV) for vitamin C were 0.93 and 1811 mg 100 g dry weight (DW) and 0.91 and 1839 mg 100 g DW using MIR and NIR spectroscopy, respectively. The coefficient of correlation and the standard error of prediction (SEP) obtained using the independent set (n = 5) were 0.65 (SEP: 2367 mg 100 g DW) and 0.73 (SEP: 4773 mg 100 g DW) using MIR and NIR spectroscopy, respectively.

CONCLUSIONS

The results obtained in this study clearly showed that it is possible to calibrate IR spectroscopic instruments for the measurement of vitamin C in KP plum powder samples. Mid-infrared spectroscopy showed the most promising results; however, Fourier transform near-infrared (FTNIR) spectroscopy also produced models capable of good quantification of this important bioactive compound and vitamin. These findings are promising in terms of using high-throughput IR spectroscopy as a routine technology to determine vitamin C in plant-based foods and derived products. © 2020 Society of Chemical Industry.