Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman).

A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.

Screening for pesticide residues in cocoa (Theobroma cacao L.) by portable infrared spectroscopy.

Rapid assessment of pesticide residues ensures cocoa bean quality and marketability. In this study, a portable FTIR instrument equipped with a triple reflection attenuated total reflectance (ATR) accessory was used to screen cocoa beans for pesticide residues. Cocoa beans (n = 75) were obtained from major cocoa growing regions of Peru and were quantified for pesticides by gas chromatography (GC) or liquid chromatography (LC) coupled with mass spectrometry (MS). The FTIR spectra were used to detect the presence of pesticides in cocoa beans or lipid fraction (butter) by using a pattern recognition (Soft Independent Modeling by Class Analogy, SIMCA) algorithm, which produced a significant discrimination for cocoa nibs (free or with pesticides). The variables related to the class grouping were assigned to the aliphatic (3200-2800 cm-1) region with an interclass distance (ICD) of 3.3. Subsequently, the concentration of pesticides in cocoa beans was predicted using a partial least squares regression analysis (PLSR), using an internal validation of the PLRS model, the cross-validation correlation coefficient (Rval = 0.954) and the cross-validation standard error (SECV = 14.9 mg/kg) were obtained. Additionally, an external validation was performed, obtaining the prediction correlation coefficient (Rpre = 0.940) and the standard error of prediction (SEP = 16.0 µg/kg) with high statistical performances, which demonstrates the excellent predictability of the PLSR model in a similar real application. The developed FTIR method presented limits of detection and quantification (LOD = 9.8 µg/kg; LOQ = 23.1 µg/kg) with four optimum factors (PC). Mid-infrared spectroscopy (MIR) offered a viable alternative for field screening of cocoa.

Monitoring Turkish white cheese ripening by portable FT-IR spectroscopy.

The biochemical metabolism during cheese ripening plays an active role in producing amino acids, organic acids, and fatty acids. Our objective was to evaluate the unique fingerprint-like infrared spectra of the soluble fractions in different solvents (water-based, methanol, and ethanol) of Turkish white cheese for rapid monitoring of cheese composition during ripening. Turkish white cheese samples were produced in a pilot plant scale using a mesophilic culture (Lactococcus lactis subsp. lactis, Lactococcus lactis subsp. cremoris), ripened for 100 days and samples were collected at 20-day intervals for analysis. Three extraction solvents (water, methanol, and ethanol) were selected to obtain soluble cheese fractions. Reference methods included gas chromatography (amino acids and fatty acid profiles), and liquid chromatography (organic acids) were used to obtain the reference results. FT-IR spectra were correlated with chromatographic data using pattern recognition analysis to develop regression and classification predictive models. All models showed a good fit (RPre ≥ 0.91) for predicting the target compounds during cheese ripening. Individual free fatty acids were predicted better in ethanol extracts (0.99 ≥ RPre ≥ 0.93, 1.95 ≥ SEP ≥ 0.38), while organic acids (0.98 ≥ RPre ≥ 0.97, 10.51 ≥ SEP ≥ 0.57) and total free amino acids (RPre = 0.99, SEP = 0.0037) were predicted better by using water-based extracts. Moreover, cheese compounds extracted with methanol provided the best SIMCA classification results in discriminating the different stages of cheese ripening. By using a simple methanolic extraction and collecting spectra with a portable FT-IR device provided a fast, simple, and cost-effective technique to monitor the ripening of white cheese and predict the levels of key compounds that play an important role in the biochemical metabolism of Turkish white cheese.

Pattern Recognition Approach for the Screening of Potential Adulteration of Traditional and Bourbon Barrel-Aged Maple Syrups by Spectral Fingerprinting and Classical Methods.

This study aims to generate predictive models based on mid-infrared and Raman spectral fingerprints to characterize unique compositional traits of traditional and bourbon barrel (BBL)-aged maple syrups, allowing for fast product authentication and detection of potential ingredient tampering. Traditional (n = 23) and BBL-aged (n = 17) maple syrup samples were provided by a local maple syrup farm, purchased from local grocery stores in Columbus, Ohio, and an online vendor. A portable FT-IR spectrometer with a triple-reflection diamond ATR and a compact benchtop Raman system (1064 nm laser) were used for spectra collection. Samples were characterized by chromatography (HPLC and GC-MS), refractometry, and Folin-Ciocalteu methods. We found the incidence of adulteration in 15% (6 out of 40) of samples that exhibited unusual sugar and/or volatile profiles. The unique spectral patterns combined with soft independent modeling of class analogy (SIMCA) identified all adulterated samples, providing a non-destructive and fast authentication of BBL and regular maple syrups and discriminated potential maple syrup adulterants. Both systems, combined with partial least squares regression (PLSR), showed good predictions for the total ˚Brix and sucrose contents of all samples.

A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp.

A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of Δ9-tetrahydrocannabinol (Δ9-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91-0.95) and a low standard error of prediction (SEP = 0.02-0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.

Screening of Acrylamide of Par-Fried Frozen French Fries Using Portable FT-IR Spectroscopy.

Current assays for acrylamide screening rely heavily on LC-MS/MS or GC-MS, techniques that are not suitable to support point of manufacturing verification because it can take several weeks to receive results from a laboratory. A portable sensor that can detect acrylamide levels in real-time would enable in-house testing to safeguard both the safety of the consumer and the economic security of the agricultural supplier. Our objective was to develop a rapid, accurate, and real-time screening technique to detect the acrylamide content in par-fried frozen French fries based on a portable infrared device. Par-fried French fries (n = 70) were manufactured at times ranging from 1 to 5.5 min at 180 °C to yield a wide range of acrylamide levels. Spectra of samples were collected using a portable FT-IR device operating from 4000 to 700 cm-1. Acrylamide was extracted using QuEChERS and quantified using uHPLC-MS/MS. Predictive algorithms were generated using partial least squares regression (PLSR). Acrylamide levels in French fries ranged from 52.0 to 812.8 µg/kg. The best performance of the prediction algorithms required transformation of the acrylamide levels using a logarithm function with models giving a coefficient of correlation (Rcv) of 0.93 and RPD as 3.8, which means the mid-IR model can be used for process control applications. Our data corroborate the potential of portable infrared devices for acrylamide screening of high-risk foods.

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers.

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (RPre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.

Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy.

Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC-MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC-MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles.

Application of Mid-Infrared Portable Spectrometer for the Rapid Determination of Trans-Fatty Acid Content in Lipid Extracts of Snack and Bakery Products.

In 2015, the US Food and Drug Administration passed a ban on the "generally recognized as safe" status of partially hydrogenated oils (PHOs), and in June 2018, PHOs were prohibited from being used. Our objective was to develop a predictive model to quantify trans-fat concentrations in bakery and snacks products using a portable mid-infrared (MIR) spectrometer. The approach was tested using 24 calibration standards (consisting of trielaidin in triolein and tripalmitin) and 87 bakery and snack products ranging from ND to 65% trans-fat. The fat was extracted by grinding products into powders and extracting the fat using petroleum ether. Gas Chromatography (AOCS Cd 14c-94) was used to determine the fatty acid profile and trans-fat content. Spectra were acquired by directly placing the fat (200 µL) onto the heated (65 ± 1°C) 5-reflection ZnSe crystal of a portable MIR spectrometer. Partial least squares regression (PLSR) models were developed using the calibration standards and extracted fats spectra targeting the signal of the C-H out-of-plane deformation band at 966 cm-1. Best model performances were obtained using the spectra of the extracted fat from bakery and snack products with the standard error of prediction of 0.5 g of trans-fats per 100 g of fat. We found that 25% of products labeled as zero trans-fat/serving did not comply with the maximum tolerance levels based on GC-FAME analysis. Portable FTIR devices operating in attenuated total reflection (ATR) mode can provide the food industry and government food inspectors with rapid, accurate, and high throughput measurements for routine screening to facilitate regulatory compliance.

Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis.

This research aims to provide simultaneous predictions of tomato paste's multiple quality traits without any sample preparation by using a field-deployable portable infrared spectrometer. A total of 1843 tomato paste samples were supplied by four different leading tomato processors in California, USA, over the tomato seasons of 2015, 2016, 2017, and 2019. The reference levels of quality traits including, natural tomato soluble solids (NTSS), pH, Bostwick consistency, titratable acidity (TA), serum viscosity, lycopene, glucose, fructose, ascorbic acid, and citric acid were determined by official methods. A portable FT-IR spectrometer with a triple-reflection diamond ATR sampling system was used to directly collect mid-infrared spectra. The calibration and external validation models were developed by using partial least square regression (PLSR). The evaluation of models was conducted on a randomly selected external validation set. A high correlation (RCV = 0.85-0.99) between the reference values and FT-IR predicted values was observed from PLSR models. The standard errors of prediction were low (SEP = 0.04-35.11), and good predictive performances (RPD = 1.8-7.3) were achieved. Proposed FT-IR technology can be ideal for routine in-plant assessment of the tomato paste quality that would provide the tomato processors with accurate results in shorter time and lower cost.

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods.

Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All-trans-lycopene and ß-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (rpre = 0.9, SEP = 1.1 mg/100 g) and PLSR (rpre = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all-trans-lycopene in fruits. Although the number of samples were limited for ß-carotene quantification, PLSR modeling showed promising results (rcv = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in ß-carotene and high in cis-lycopene).

Ex Vivo and In Vivo Assessment of the Penetration of Topically Applied Anthocyanins Utilizing ATR-FTIR/PLS Regression Models and HPLC-PDA-MS.

Anthocyanins are natural colorants with antioxidant properties, shown to inhibit photoaging reactions and reduce symptoms of some skin diseases. However, little is known about their penetration through the stratum corneum, a prerequisite for bioactivity. The aim was to investigate anthocyanin penetration from lipophilic cosmetic formulations through the skin using a porcine ear model and human volunteers. ATR-FTIR/PLS regression and HPLC-PDA-MS were used to analyze anthocyanin permeation through the stratum corneum. Penetration of all anthocyanins was evident and correlated with molecular weight and hydrophilicity. Lower-molecular-weight (MW) anthocyanins from elderberry (449-581 Da) were more permeable within the skin in both ex vivo and in vivo models (Kp = 2.3-2.4 × 10-4 cm h-1) than the larger anthocyanins (933-1019 Da) from red radish (Kp = 2.0-2.1 × 10-4 cm h-1). Elderberry and red radish anthocyanins were found at all levels of the stratum corneum and at depths for activity as bioactive ingredients for skin health.

Nutrient intake and balancing among female Colobus angolensis palliatus inhabiting structurally distinct forest areas: Effects of group, season, and reproductive state.

Understanding intraspecific behavioral and dietary variation is critical for assessing primate populations' abilities to persist in habitats characterized by increasing anthropogenic disturbances. While it is evident that some species exhibit considerable dietary flexibility (in terms of species-specific plant parts) in relation to habitat disturbance, it is unclear if primates are characterized by similar variation and flexibility regarding nutrient intake. This study examined the effects of group, season, and reproductive state on nutrient intake and balancing in adult female Colobus angolensis palliatus in the Diani Forest, Kenya. During July 2014 to December 2015, estimates of nutrient intake were recorded for eight females from three groups inhabiting structurally and ecologically distinct forest areas differing in tree species composition and density. There were differences in metabolizable energy (ME) and macronutrient intakes among groups, seasons, and reproductive states. Most notably, females inhabiting one of the more disturbed forest areas consumed less ME and macronutrients compared to females in the more intact forest area. Contrary to prediction, females in early lactation consumed significantly less ME and macronutrients compared to non-lactating and late lactation females. Despite differences in macronutrient intake, the relative contribution of macronutrients to ME were generally more conservative among groups, seasons, and reproductive states. Average daily intake ratios of non-protein energy to available protein ranged from approximately 3.5:1-4.3:1 among groups. These results indicate that female C. a. palliatus demonstrate a consistent nutrient balancing strategy despite significant intergroup differences in consumption of species-specific plant parts. Data from additional colobine species inhabiting different forest types are required to assess the extent to which nutrient balancing is constrained by phylogeny or is more flexible to local ecological conditions.

Corrigendum: Identification of Quercus agrifolia (coast live oak) resistant to the invasive pathogen Phytophthora ramorum in native stands using Fourier-transform infrared (FT-IR) spectroscopy.

[This corrects the article on p. 521 in vol. 5, PMID: 25352852.].

A pilot study of health and wellness coaching for fibromyalgia.

BACKGROUND: The purpose of this study was to test the hypothesis that a health and wellness coaching (HWC)-based intervention for fibromyalgia (FM) would result in sustained improvements in health and quality of life, and reductions in health care utilization. METHODS: Nine female subjects meeting American College of Rheumatology criteria for a diagnosis of primary FM were studied. The HWC protocol had two components, which were delivered telephonically over a twelve-month period. First, each patient met individually with a coach during the 12 month study at the patient's preference of schedule and frequency (Range:22-32 × 45-min sessions). Coaches were health professionals trained in health and wellness coaching tasks, knowledge, and skills. Second, each patient participated in bimonthly (first six months) and monthly (second six months) group classes on self-coaching strategies during the 12 month study. Prior to the intervention, and after 6 months and 12 months of coaching, the Revised Fibromyalgia Impact Questionnaire (FIQR) was used to measure health and quality of life, and the Brief Pain Inventory-Short Form (BPI) was used to measure pain intensity and interference with function. Total and rheumatology-related health encounters were documented using electronic medical records. Data were analyzed using repeated measures ANOVA. RESULTS: All nine patients finished the HWC protocol. FIQR scores improved by 35 % (P = 0.001). BPI scores decreased by 32 % overall (P = 0.006), 31 % for severity (P = 0.02), and 44 % for interference (P = 0.006). Health care utilization declined by 86 % (P = 0.006) for total and 78 % (P < 0.0001) for rheumatology-related encounters. CONCLUSION: The HWC program added to standard FM therapy produced clinically significant improvements in quality of life measures (FIQR), pain (BPI), and marked reductions in health care utilization. Such improvements do not typically occur spontaneously in FM patients, suggesting that HWC deserves further consideration as an intervention for FM.

Improving the screening of potato breeding lines for specific nutritional traits using portable mid-infrared spectroscopy and multivariate analysis.

Efficient selection of potato varieties with enhanced nutritional quality requires simple, accurate and cost effective assays to obtain tuber chemical composition information. In this study, 75 Andean native potato samples from 7 Solanum species with different colors were characterized and quantified for their anthocyanin, phenolics and sugar content using traditional reference methods. IR (infrared) spectra of potato extracts were collected using a portable infrared system and partial least squares regression (PLSR) calibration models were developed. These models were validated using both full cross-validation and an independent sample set giving strong linear correlation coefficients of prediction (rPred)>0.91 and standard error of prediction (SEP) of 24mg/100g phenolics, 7mg/100g monomeric anthocyanins, 0.1g/100g reducing sugars and 0.12g/100g sucrose. Overall, portable infrared system with PLSR showed great potential to facilitate potato breeding and certain aspects of crop management, material selection for potato processing and related research by providing alternative prediction models.

Quantifying soluble carbohydrates in tropical leaves using a portable mid-infrared sensor: Implications for primate feeding ecology.

Identifying the nutritional composition of food items has significant ramifications for primate feeding ecology, which, in turn, influences investigations of primate sociality, cognition, and conservation. The aim of our study was to analyze water soluble carbohydrate (WSC) concentrations in the leaves of trees common to the Diani Forest of Kenya. Many of these leaves are consumed by black and white colobus monkeys (Colobus angolensis palliatus). We assessed whether the infrared spectral data collected using a portable spectrometer can be used to accurately predict WSC concentrations. WSC content was first quantified using the phenol-sulfuric acid method for young and mature leaves of 24 species and ranged from 1.15% to 9.16% dry weight. Spectral data were recorded with a spectrometer equipped with an attenuated total reflectance accessory (Agilent Cary 630) and analyzed using partial least squares regression. The spectral region from 1600 cm(-1) to 1000 cm(-1) gave unique polysaccharide bands associated with carboxyl, acetyl, and glycosidic linkages of sugar residues. The multivariate analysis gave excellent performance parameters with correlation coefficient (r(2) ) of 0.95 and standard error of cross-validation of 0.6% WSC. We found that IR spectroscopy provides a rapid and accurate technique for analyzing WSC concentrations and offers primatologists many advantages over wet chemistry methods for analyzing nutritional composition. Am. J. Primatol. 78:701-706, 2016. © 2016 Wiley Periodicals, Inc.

Understanding Tomato Peelability.

Approximately 75% of all tomatoes in the United States are consumed as processed and 25% as fresh. One of the first steps during processing involves removal of the peel and, unfortunately, more than 25% of the fruits (as measured by total weight) can be lost due to overpeeling. Additionally, conventional peeling applications have a negative environmental impact. Given the great potential economic benefits, many scientists have conducted research to attempt optimizing or predicting peeling performance when processing tomatoes. The literature regarding tomato peelability is contradictory in many cases; and several topics have been subject to ample debate over the years. Divergent conclusions are probably not due to faulty investigations, but rather to the extreme variability found among tomato cultivars, the effect of growing seasons, and maybe even the effect of climatic conditions on the day of harvest or during transportation to the processing plants. This review provides an in-depth background needed for a better understanding of tomato physiology, maturation, and composition, as these could possibly influence the ease of peeling or "peelability." The research studies directly involved with peeling tomatoes and predicting peelability are discussed in this paper as well. Different peeling methods, peeling grading scales, and fruit tagging procedures are presented, as well as experiments evaluating the effect that fruit defects, maturity, growing conditions, and other factors can have on the ease of peeling. Novel approaches for peelability prediction by means of spectroscopic and magnetic resonance technology are also discussed in this review.

Authentication of Whey Protein Powders by Portable Mid-Infrared Spectrometers Combined with Pattern Recognition Analysis.

UNLABELLED: The objective of this study was to develop a simple and rapid method to differentiate whey protein types (WPC, WPI, and WPH) used for beverage manufacturing by combining the spectral signature collected from portable mid-infrared spectrometers and pattern recognition analysis. Whey protein powders from different suppliers are produced using a large number of processing and compositional variables, resulting in variation in composition, concentration, protein structure, and thus functionality. Whey protein powders including whey protein isolates, whey protein concentrates and whey protein hydrolysates were obtained from different suppliers and their spectra collected using portable mid-infrared spectrometers (single and triple reflection) by pressing the powder onto an Attenuated Total Reflectance (ATR) diamond crystal with a pressure clamp. Spectra were analyzed by soft independent modeling of class analogy (SIMCA) generating a classification model showing the ability to differentiate whey protein types by forming tight clusters with interclass distance values of >3, considered to be significantly different from each other. The major bands centered at 1640 and 1580 cm(-1) were responsible for separation and were associated with differences in amide I and amide II vibrations of proteins, respectively. Another important band in whey protein clustering was associated with carboxylate vibrations of acidic amino acids (∼1570 cm(-1)). The use of a portable mid-IR spectrometer combined with pattern recognition analysis showed potential for discriminating whey protein ingredients that can help to streamline the analytical procedure so that it is more applicable for field-based screening of ingredients. PRACTICAL APPLICATION: A rapid, simple and accurate method was developed to authenticate commercial whey protein products by using portable mid-infrared spectrometers combined with chemometrics, which could help ensure the functionality of whey protein ingredients in food applications.

Application of a portable infrared instrument for simultaneous analysis of sugars, asparagine and glutamine levels in raw potato tubers.

The level of reducing sugars and asparagine in raw potatoes is critical for potato breeders and the food industry for production of commonly consumed food products including potato chips and French fries. Our objective was to evaluate the use of a portable infrared instrument for the rapid quantitation of major sugars and amino acids in raw potato tubers using single-bounce attenuated total reflectance (ATR) and dial path accessories as an alternative to time-consuming chromatographic techniques. Samples representing a total of 84 experimental and commercial potato varieties harvested in two consecutive growing seasons (2012 and 2013) were used in this study. Samples had wide ranges of sugars determined by HPLC-RID (non-detectable (ND)-7.7 mg glucose, ND-9.4 mg fructose and 0.4-5.4 mg sucrose per 1 g fresh weight), and asparagine and glutamine levels determined by GC-FID (0.7-2.9 mg and 0.3-1.7 mg per 1 g fresh weight). Infrared spectra collected from 64 varieties were used to create partial least squares regression (PLSR) calibration models that predicted the sugar and amino acid levels in an independent set of 16 validation potato varieties. Excellent linear correlations between infrared predicted and reference values were obtained. PLSR models had a high correlation coefficient of prediction (rPred >0.95) and residual predictive deviation (RPD) values ranging between 3.1 and 5.5. Overall, the results indicated that the models could be used to simultaneously predict sugars, free asparagine and glutamine levels in the raw tubers, significantly benefiting potato breeding, certain aspects of crop management, crop production and research.