Standard Error Of Cross-validation Research Articles

Comparative Accuracy of In Vitro Rumen Fermentation and Enzymatic Methodologies for Determination of Undigested Neutral Detergent Fiber in Forages and Development of Predictive Equations Using NIRS

Undigested neutral detergent fiber (uNDF) is becoming more widely recognized as an important fiber fraction in forage quality assessment because it explains a portion of NDF that is inaccessible to digestion in the ruminant digestive system and is, thus, important in modeling the digestion kinetics of the potentially degradable component of NDF. In experiment 1, uNDF was determined in several forage species in order to compare the accuracy of two reference methods: (1) a long-term in vitro ruminal fermentation (240 h) using an Ankom DaisyII incubator and (2) a multi-step enzymatic method without ruminal fluid. The objective of experiment 2 was to construct predictive equations for uNDF estimation using acid detergent lignin (ADL) and near-infrared reflectance spectroscopy (NIRS) in a pool (n = 264) of alfalfa hay, timothy hay, and tall fescue straw, using the most accurate reference method selected in experiment 1. Partial least squares regression analysis was used to calibrate the reference values against NIRS spectra. Several indicators were used to assess the performance of validation results, including standard error of cross-validation (SECrV), coefficient of determination of cross-validation (R2CrV), and ratio percentage deviation (RPD). The findings of experiment 1 suggested that, relative to the in vitro ruminal methodology, the enzymatic approach overestimated uNDF concentration of forages. Repeatability coefficient was also greater when uNDF was determined using the in vitro versus enzymatic procedure, potentially disqualifying the enzymatic method for the uNDF analysis in forages. In experiment 2, a poor relationship was established between ADL and uNDF (R2 < 0.60), suggesting the inadequacy of ADL parameter to represent the uNDF pool size in these forages. The best predictive equation using NIRS was obtained for alfalfa hay (R2CrV = 0.92; SECrV = 1.16; RPD = 3.57), using the in vitro fermentation as a reference method. The predictive equations were moderately accurate for timothy hay (R2CrV = 0.80; SECrV = 1.31; RPD = 2.08) and tall fescue straw (R2CrV = 0.79; SECrV = 1.38; RPD = 2.18). Our findings suggested the inadequacy of the enzymatic procedure in accurately determining uNDF concentration of forages as compared with the in vitro rumen fermentation protocol. Although the NIRS equations developed using the alfalfa hay dataset were more accurate than that of timothy hay and tall fescue straw, the validation results verified applicability of the equations as a fast screening tool for qualitative prediction of uNDF in these forages, which is important in commercial settings.

Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics.

In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.

158 Predicting Intake and Digestibility of Nutrients in Beef Cattle fed High Forage Diets Using Near Infrared Spectroscopy (Nirs) of Feces and Internal Markers

Abstract Objectives of this study were to determine the potential of near infrared-spectroscopy (NIRS) scanning of feces to predict diet digestibility and intake in beef cattle fed high forage diets, and to investigate the use of acid detergent lignin (ADL) and undigestible neutral detergent fiber (uNDF) as internal markers to determine digestibility. Beef heifers were fed 12 different forage-based diets ( > 95% forage dry matter basis) in 3 total collection studies, resulting in individual fecal samples and related spectra (n = 135) corresponding to apparent total tract digestibility (aTTD) and intake data. Dried and ground fecal samples were scanned using a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). Modified partial least squares (MPLS) regression was performed to develop equations to predict digestibility [dry matter (DM), organic matter (OM), NDF, and nitrogen (N)], and intake [DM, OM, NDF, N, uNDF]. Digestibility prediction equations for DM, OM, NDF, and N resulted in R2CV or 0.72, 0.65, 0.74 and 0.69, respectively, and standard error of cross validation (SECV) between 2.20 to 2.82. Intake calibrations for DM, OM, N, NDF, ADL and uNDF resulted in R2CV values between 0.59 and 0.91, SECV for intake (kg/d) of 1.12, 1.10, 0.02, 0.69, 0.06, 0.24, respectively, SECV for intake % body weight (BW) between 0.00 and 0.16; and SECV for g/kg BW0.75 between 0.60 and 0.86. Significant correlations (P < 0.01) were identified between NDFD determined by aTTD and by the internal marker ADL (r = 0.72) or uNDF (r = 0.57). Developed calibrations predicting marker calculated digestibility resulted in R2CV(SECV) values of 0.85(2.70) and 0.80(2.46) for lignin and uNDF, respectively. We confirm the potential of NIRS to predict digestibility and intake of cattle fed high forage diets. Future steps include further validation of the intake calibration equations using internal marker and energetics modeling.

Estimation of in vitro digestibility and fermentation of elephant grass by near infrared spectroscopy

AbstractThe methods used to evaluate kinetic parameters of ruminant feeds can be in situ and in vitro. For both methods, it is necessary to maintain cannulated animals in order to collect the inoculum from the rumen, which has been under strong pressure from society, in terms of animal welfare policies, to avoid this type of practices. This work aimed to evaluate the estimation of in vitro digestibility and fermentation of elephant grass forage by near infrared reflectance spectroscopy (NIRS), as a fast and noninvasive alternative to the in vitro method. The experimental design was in randomized blocks with three repetitions. The treatments were arranged in a split‐plot scheme, with 13 genotypes in the plot and 15 regrowth ages in the subplot. Crude protein (CP) and neutral detergent fiber (NDF) contents; in vitro dry matter (IVDMD) and NDF (IVNDFD) digestibilities; and total volume (Vt), gas production rate (μ) and half‐life conventional analyses were determined for pre‐dried elephant grass forage samples. For the evaluation of NIRS models performance, coefficient of determination (R2cv) and standard error of cross validation (SECV) were used. As the regrowth age advanced, there was a linear increase in the NDF content and a linear reduction in the IVDMD, IVNDFD and Vt values. The NIRS estimates proved to be adequate for IVDMD (R2cv = .95), IVNDFD (R2cv = .85), Vt (R2cv = .81) and gas production at 48 (R2cv = .82), 72 (R2cv = .85) and 96 h (R2cv = .84), confirming as an alternative to the in vitro methods that dispense with the use of cannulated animals.

Shedding light on human tissue (in vivo) to predict satiation, satiety, and food intake using near infrared reflectance spectroscopy: A preliminary study

The aim of this study was to evaluate the ability of a portable near infrared (NIR) instrument to collect the spectra in vivo of different tissues in healthy individuals and to relate their spectral information with food and energy intake, satiation, and satiety data. In this study, a hand-held NIR instrument was used to collect the spectra of different human tissues (e.g. arm, ear, face, jaw and wrist) with partial least squares (PLS) regression used to relate the NIR data with food and energy intake, satiation, and satiety measured in healthy individuals. The coefficient of determination in cross-validation (R2CV) and the standard error in cross validation (SECV) for the prediction of satiety ranged between 0.58 and 0.62 and 223.3–235.0 total area under the curve (AUC), respectively, depending on the tissue analysed. The PLS cross-validation models based on the NIR spectra collected in both the arm and face tissues gave the best prediction of food intake (R2CV 0.47–0.51, SECV 110.8-115 g). No workable calibrations were developed for the prediction of satiation, which might be associated with the inherent complexity of this parameter as well as the experimental conditions used to collect the data (e.g. type of tissue analysed). These results demonstrated the potential ability of in vivo NIR spectroscopy to identify tissue differences associated with satiety and food intake in individuals. However, a wider variety of food types, diets, and human subjects (samples) are needed to develop robust relationships between the NIR spectra of a tissue with both satiety and food intake.

Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13197-022-05456-7.

Prediction of crosslink density of prevulcanised latex using NIR Spectroscopy based on combination of fractional order derivative (FOD) and variable selection methods

Rapid method in measurement of crosslink density is required in factory. The objective of this study was to develop the prediction model of crosslink densities based on near infrared (NIR) spectroscopy method. The prediction models were developed using partial least squares regression (PLSR) with spectral pre-treatment of fractional order derivatives (FOD) and variable selection methods including successive project algorithm (SPA) and genetic algorithm (GA). The result demonstrated that prevulcanised (PV) latex model had higher accuracy than that of PV50 latex model. Effective model in predicting crosslink densities of PV and PV50 latices could be pre-processed with FOD=1 and 0.75, respectively. The prediction model generated with full wavelength had the standard error of cross validation (SECV) of 3.21% and 3.52%, respectively. The model performance of PV latex could improve with variable selection method of GA which reduced the SECV from 3.21% to 3.17% and number of wavelengths reduced from 1059 to 937. The model performance of PV50 could not reduce by using the variable selection method. However, the GA could reduce the number of wavelengths from 1059 to 216.

Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics.

The aim of this study was to evaluate the ability of mid-infrared (MIR) spectroscopy combined with chemometrics to analyze unstimulated saliva as a method to predict satiety in healthy participants. This study also evaluated features in saliva that were related to individual perceptions of human–food interactions. The coefficient of determination (R2) and standard error in cross validation (SECV) for the prediction of satiety in all saliva samples were 0.62 and 225.7 satiety area under the curve (AUC), respectively. A correlation between saliva and satiety was found, however, the quantitative prediction of satiety using unstimulated saliva was not robust. Differences in the MIR spectra of saliva between low and high satiety groups, were observed in the following frequency ratios: 1542/2060 cm−1 (total protein), 1637/3097 cm−1 (α-amino acids), and 1637/616 (chlorides) cm−1. In addition, good to excellent models were obtained for the prediction of satiety groups defined as low or high satiety participants (R2 0.92 and SECV 0.10), demonstrating that this method could be used to identify low or high satiety perception types and to select participants for appetite studies. Although quantitative PLS calibration models were not achieved, a qualitative model for the prediction of low and high satiety perception types was obtained using PLS-DA. Furthermore, this study showed that it might be possible to evaluate human/food interactions using MIR spectroscopy as a rapid and cost-effective tool.

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

Unexpected climatic conditions or extreme climatic events in vineyards are a worldwide problem that requires accurate spatial and temporal monitoring. Satellite-based remote sensing is an important source of data to assess this challenge in a climate-change context. This paper provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess heatwave impacts in vineyards at a regional scale. Multi-way partial least squares (N-PLS) regression was used as a supervised technique to predict the intensity of damage caused to vineyards by the heatwave phenomenon that impacted the vineyards in the south of France in 2019. The model was developed based on available ground truth data of yield losses for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The model showed a performance accuracy (R2) of 0.56 in the calibration set and of 0.66 in the validation set, with a standard error of cross-validation in the calibration set of 12.4% and a standard error of the prediction of yield losses in the validation set of 10.7. The model was applied at a regional scale on 4978 vineyard blocks to predict yield losses using spectral and temporal attributes. The prediction of the yield loss due to heat stress at a regional scale was related to the spatial pattern of maximum temperatures recorded during the extreme weather event. This relation was confirmed by a chi-square test (p < 5%). The introduction of N-PLS insights into the analysis enables the characterisation of heat stress responses in vineyards and the identification of spectro-temporal profiles relevant for understanding the effects of heatwaves on vine blocks at a regional scale.

Color approach to the analysis of white crystal cane sugar for the detection of solid impurities.

Sugar is consumed worldwide and so the quality control of sugar cane is necessary. Solid impurities are an inherent part of industrial sugar processing. Dark particles and adulteration with sand must be controlled. Sixty-four samples of white crystal cane sugar analysis in the presence of both kinds of impurities (dark particles and sand) were assessed using an affordable digital image system and a multivariate calibration strategy. The quality parameters for the multivariate calibration models obtained to estimate sugar content were remarkable. Color descriptors from digital images allowed identification of different levels of sugar content for the following three ranges: 0-49.99 wt%, 50.03-78.99 wt%, and 82.99-100 wt%. The multivariate model using red (R), green (G), Blue (B), and luminosity (L) color descriptors showed low standard errors of cross-validation (SECV) and validation (SEV) of 7.63 and 6.01 wt%, respectively. The method is affordable and reliable, and might aid quick screening in situations where access to a laboratory or instrumentation is restricted. © 2021 Society of Chemical Industry.

Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation

Near Infrared Reflectance (NIR) spectroscopy provides a rapid, easy to use, and non-destructive alternative to the methods traditionally employed in the determination of the chemical composition of food stuff, since it does not require reagents, time consuming procedures based on manual labor, and can determine multiple parameters simultaneously. Therefore, the use of NIR spectroscopy was evaluated in the quantification of the moisture, fat, and crude protein content of raw bovine meat, in order to support the development for industrial use of an Azorean Protected Geographical Indication (PGI) beef hamburgers, with a target fat content of 20 g/100 g. Sixty-six meat samples were studied, including the carcass cut of neck, shin, brisket, chuck and blade, flanks (thin flank and forequarter flank), plate ribs, ribs and flanks, mixture of unspecified cuts (minced meat), and commercial hamburgers. The samples were chemically analyzed according to standard methods, for moisture (drying method), total fat (solvent extract), and crude protein content (micro-Kjeldahl method). It was observed that the samples had a high fat content heterogeneity. The spectra of all samples were collected (400–2500 nm) and a calibration was performed using partial least squares regression (PLS) and cross validation. Several spectral pre-treatment techniques were applied, including the standard normal variate (SNV). The best calibration for the chemical properties under evaluation showed coefficient of determination (R2) and standard error of cross validation (SECV) values of 0.93 and 1.25%, respectively, for fat content, 0.89 and 0.99% for crude protein, and 0.72 and 2.18% for moisture. It was concluded that the use of NIR spectroscopy provides a good estimate of the fat and protein content of raw bovine meat.

Evaluation of Chemical Quality on Juices and Wine Produced from Mamao Fruit (Antidesma Puncticulatum Miq.) Within Near-Infrared Spectroscopy

The chemical quality of juices and wine produced from Mamao fruit was evaluated by Fourier transform near-infrared (FT-NIR). The calibration equation was created by the cross-validation method to be si+mulated the accuracy. Statistical values composed of correlation coefficient (R), standard error of cross-validation (SECV) and bias were used. Brix values and acidity values of Mao juice and the Brix, acidity, and alcohol values of Mao wine products were evaluated through the standard and cross-validation relation. It was found that was observed with NIR spectrometer to be absorbed in the same IR wavelength (1450 nm) which indicated that the water is the main composition. Based on FT-NIR analysis, the spectrum latices of juices and wine were revealed in the same range of the absorption bands at 1450 nm and 1940 nm to be confirmed the water composition. Also, the FT-NIR spectra from region 2258-2312 nm in Mao wine product have been predicted to the Ethanol functions.

Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using near-infrared spectroscopy.

Knowledge of the amounts and digestibility of amino acids in pig feedstuffs is essential for calculating the appropriate inclusion level in a complete diet. Wet chemical analysis and in vivo digestibility trials are time-consuming and costly and cannot be used for routine assessment. Near-infrared spectroscopy (NIRS) offers a rapid, cost effective and environmentally friendly method for evaluating feedstuffs. Calibrations models were developed using NIRS to predict the content of crude protein and 18 amino acids from a wide range of feedstuffs used in pig production (n = 607). The samples ranged from single feed ingredients (containing amino acids from 0.3 to 129.8 g/kg of dry matter) to feed mixtures (containing amino acids from 1.2 to 53.2 g/kg of dry matter). The predictive ability of the calibrations was tested with an independent dataset (n = 150) and with cross-validation. Furthermore, we compare these calibrations with calibrations developed on more narrowly defined groups of samples and with predictions from regression analysis of crude protein. The models were able to predict the concentrations of crude protein and 18 amino acids with good levels of precision and high coefficients of determination for calibration (RSQ CAL) from 0.91 to 0.99 and validation (RSQVAL) from 0.87 to 0.97. Calibration models were able to predict all amino acids except tryptophan and valine with greater accuracy than those from protein regression. We also developed calibration models to predict the apparent ileal and total tract digestibility of protein and amino acids. With the exception of tryptophan, RSQ values (>0.7) and standard error of cross validation (SECV) values (<5%) were obtained for the digestibility of most of the amino acids. In conclusion, NIRS can be used to predict crude protein and amino acid concentrations from a wide range of single ingredients and feed mixtures used for pig diets without separate models for each feedstuff. The digestibility of protein and amino acids can be predicted with an acceptable accuracy to be useful in formulating pig diets.

The usefulness of NIRS calibrations based on feed and feces spectra to predict nutrient content, digestibility and net energy of pig feeds

Near infrared spectroscopy (NIRS) is widely used to predict the potential nutritive value of feeds. Near infrared spectroscopy calibrations based on a combination of feed and feces spectra may reflect the effective nutritive value. We investigated the usefulness and accuracy of NIRS based on combined spectra from pig feed and feces to predict their chemical composition as well as nutrient digestibility and net energy (NE) content. A total of 62 feeds and 310 freeze-dried feces samples (5 per feed) from 3 in vivo digestibility experiments were used. First, calibrations based on either feed spectra or feces spectra only were developed to predict their chemical composition. Then, calibrations based on combined feed and feces spectra were developed, thereby comparing 3 possibilities: merging, subtracting or averaging spectra. The calibrations were evaluated by cross-validation either by leave-one-feed-out or by splitting the dataset in 4 random groups. Most of the chemical parameters of the feed and the feces could be predicted accurately (residual prediction deviation, RPD ~ 3, R2 > 0.8), but predictions for feed were somewhat less accurate for crude protein (RPD = 2.1), non-starch polysaccharides (RPD = 2.0) and sugar (RPD = 1.0); and for feces, crude fiber (RPD = 2.1), organic matter (RPD = 2.2) and gross energy (RPD = 1.8). Net energy was better estimated using feed spectra than feces spectra, with a standard error of cross validation (SECV) of 0.33 and 0.46 MJ/kg, respectively. The digestibility of nutrients was poorly estimated from both calibrations based on feed or feces spectra alone (RPD ~ 1.5). The combination of spectra resulted in an overall better estimation of the digestibility and NE especially with merging and subtracting; for NE, these combinations resulted in an SECV of 0.26 and 0.27 MJ/kg, respectively. For all calibrations based on feces spectra, either singular or combined, the leave-one-feed-out cross-validation resulted in higher SECV than the validation with 4 random groups. However, the latter cross-validation method may lead to over-optimistic results as the group to be validated may contain spectra which are not independent.

Quantification of the Geranium Essential Oil, Palmarosa Essential Oil and Phenylethyl Alcohol in Rosa damascena Essential Oil Using ATR-FTIR Spectroscopy Combined with Chemometrics

Rosa damascena essential oil is an essential oil that has the greatest industrial importance due to its unique quality properties. The study used ATR-FTIR (attenuated total reflectance-Fourier transform infrared) spectroscopy coupled with chemometrics of PLSR (partial least squares regression) and PCR (principal component regression) for quantification of probable adulterants of geranium essential oil (GEO), palmarosa essential oil (PEO) and phenyl ethyl alcohol (PEOH). Hierarchical cluster analysis was performed to observe the classification pattern of Rosa damascena essential oil, spiked samples and adulterants. Rosa damascena essential oil was spiked with each adulterant at concentrations of 0–100% (v/v). Excellent R2 (regression coefficient) values (≥0.96) were obtained in all PLSR and PCR cross-validation models. The SECV (standard error of cross-validation) values ranged between 0.43 and 4.15. The lowest SECV and bias values were observed in the PLSR and PCR models, which were built by using the raw FTIR spectra of all samples. Hierarchical cluster analysis through Ward’s algorithm and Euclidian distance had high potential to observe the classification pattern of all adulterated and authentic samples. In conclusion, the combination of ATR-FTIR spectroscopy with multivariate analysis can be used for rapid, cost-effective, easy, reliable and high-throughput detection of GEO, PEO and PEOH in Rosa damascena essential oil.

Development of NIR spectroscopy based prediction models for nutritional profiling of pearl millet (Pennisetum glaucum (L.)) R.Br: A chemometrics approach

Pearl millet can be viably used for food diversification due to its balanced nutritional composition. Nutritional parameters are conventionally assessed using labour and time-intensive strenuous conventional methods for germplasm screening. Near-infrared reflectance spectroscopy (NIRS) uses near-infrared sections of the electromagnetic spectrum for precise and speedy determination of biochemical parameters for large germplasm. MPLS (Modified Partial Least Squares) regression based NIRS prediction models were developed to assess starch, resistant starch, amylose, protein, oil, total dietary fibre, phenolics, total soluble sugars, phytic acid for high throughput screening of pearl millet germplasm. Mathematical treatments executed by permutation and combinations for calibrating the model, where 2nd, 3rd, and 4th derivatives produced the best results. Treatments “4,5,4,1” was finalized for protein, oil, resistant starch, total dietary fibre, “3,4,4,1” for phenolics, “2,8,4,1” for amylose, “2,4,4,1” for phytic acid, “4,7,4,1” for total soluble sugars and “2,8,4,1” for starch. Treatments with the highest 1-Variance ratio, RSQinternal (coefficient of determination) values, lowest SEC(V) (standard error of cross-validation), SEP(C) (standard error of performance) were identified for subsequent validation. External validation determined the prediction accuracy based on RSQexternal, RPD (residual prediction deviation), SD (standard deviation), p-value ≥ 0.05 and low SEP(C).

Exploring the relationships between oral sensory physiology and oral processing with mid infrared spectra of saliva

Saliva is a critical component during eating, as it performs an important role in sensory perception and processing of food. However, the individual variations and relationships between oral sensory physiology, oral processing and saliva with food are not well understood. The aim of this study was to analyse as well as to interpret individual variations and relationships between the mid infrared (MIR) spectra of saliva and oral physiology and behaviour (e.g. fungiform papillae, saliva flow, and oral processing time). The fungiform papillae density of the tongue, resting saliva flow rate and oral processing time were measured in participants from a sensory study (n = 52). The MIR spectra of unstimulated saliva samples sourced from participants were collected and used to develop partial least squares (PLS) regression models. These models were utilised to interpret the relationships and individual variations between saliva quantity, oral sensing physiology, and oral processing with the saliva spectra. The coefficient of determination (R2) and the standard error in cross validation (SECV) obtained were for saliva flow rate (R2 0.65–0.83, SECV 0.25–0.31), papillae density of tongue (R2 0.73–0.83, SECV 7.4–11.2), and oral processing time (R2 0.7–0.87, SECV 1.66–3.04). Optimal models were obtained when participants were divided into groups namely young male, young female, and older participants. Different PLS loadings in the MIR fingerprint regions were observed between different groups, which demonstrates that different components of saliva connect with oral sensing physiology and oral processing differences among different demographic groups. These results indicated the potential of the salivary fingerprint spectra to understand the consumer eating experience and food selection more deeply, providing a tool in marketing and sensory experiments.

Prediction of the intramuscular connective tissue components of fresh and freeze-dried samples by near infrared spectroscopy

This study compared the performance of near-infrared spectroscopy (NIRS) models on fresh and freeze-dried beef muscle samples to predict intramuscular connective tissue (IMCT) components and to determine whether the accuracy of the models differed among different muscles from beef cattle. The hypothesis was that the water content of muscle samples would negatively influence the accuracy of the models, which would differ among muscles. Fresh and freeze-dried samples (n = 171) of four muscles were used to develop NIRS models to predict the contents IMCT. For the total collagen content, the standard error of cross validation (SECV) for model using freeze-dried samples (0.75 mg OH-prol/g DM) was lower than that for model using fresh samples (0.84 mg OH-prol/g DM). For cross-links and proteoglycans, the SECV for models using fresh sample spectra was lower than that for models using freeze-dried sample spectra. The accuracy of the prediction of the models also differed among predicted muscle types.

Rapid prediction of chemical composition and degree of starch cook of multi-species aquafeeds by near infrared spectroscopy

Ensuring aquafeeds meet the expected nutritional and physical specifications for a species is paramount in research and for the industry. This study aimed to examine the feasibility of predicting the proximate composition and starch gelatinisation (or cook) of aquaculture feeds (aquafeeds) regardless of their intended target species by near infrared (NIR) spectroscopy. Aquafeed samples used for nutrition experiments on various aquatic species with different nutritional requirements, as well as aquafeeds manufactured under varying extrusion conditions and steaming time to generate variable starch cook were used in this study. The various size pellets were ground before scanning by NIR spectroscopy, then models were developed to estimate dry matter, ash, total lipid, crude protein, and gross energy as well as starch cook. Proximate prediction models were successfully produced for diets with R2 values between 0.88 and 0.97 (standard error of cross-validation (SECV) 0.43 to 1.46, residual predictive deviation (RPD) 4.6 to 15.6), while starch cook models were produced with R2 values between 0.91 and 0.97 (SECV 3.60 to 5.76, RPD 1.2 to 1.9). The developed NIR models allow rapid monitoring of the nutritional composition, as well as starch cook, one of the major physical properties of aquafeeds. Models that provide rapid quality control assessment of diet characteristics is highly desirable in aquaculture research and the aquafeed industry.

Can Infrared Spectroscopy Detect Adulteration of Kakadu Plum (Terminalia ferdinandiana) Dry Powder with Synthetic Ascorbic Acid?

Kakadu plum (Terminalia ferdinandiana) fruit is characterised by its high levels of natural ascorbic acid compared with other domesticated plants in the world (e.g. more than 75 times that of oranges). The concentration of ascorbic acid is one of the main factors that define the price of this fruit (as fresh or dry powder) in the market. As many other specialty foods and commodities, this food is not exempt against adulteration. Adulteration can be simply performed by the addition of water (dilution), low-quality fruit and synthetic ascorbic acid and other chemical products. The ability of attenuated total reflectance mid infrared spectroscopy (ATR-MIR) was evaluated as a tool to detect the level of adulteration of Kakadu plum powder with a synthetic source of ascorbic acid. The coefficient of determination (R2) and the standard error of cross validation (SECV) obtained for the prediction of level and source of adulteration were 0.85 (3.2%), and 0.83 (0.30%) respectively. This study demonstrated that the integration of ATR-MIR spectroscopy with chemometric analysis could be a valuable method to identify the adulteration of Kakadu plum powder with synthetic sources of ascorbic acid.