Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

This study was carried out for post-mortem non-destructive prediction of water holding capacity (WHC) in fresh beef using near infrared (NIR) hyperspectral imaging. Hyperspectral images were acquired for different beef samples originated from different breeds and different muscles and their spectral signatures were extracted. Both principal component analysis (PCA) and partial least squares regression (PLSR) models were developed to obtain an overview of the systematic spectral variations and to correlate spectral data of beef samples to its real WHC estimated by drip loss method. Partial least squares modeling resulted in a coefficient of determination (R_CV²) of 0.89 and standard error estimated by cross validation (SECV) of 0.26%. The PLSR loadings showed that there are some important absorption peaks throughout the whole spectral range that had the greatest influence on the predictive models. Six wavelengths (940, 997, 1144, 1214, 1342, and 1443 nm) were then chosen as important wavelengths to build a new PLS prediction model. The new model led to a coefficient of determination (R_CV²) of 0.87 and standard error estimated by cross validation (SECV) of 0.28%. Image processing algorithm was then developed to transfer the predicting model to each pixel in the image for visualizing drip loss in all portions of the sample. The results showed that hyperspectral imaging has the potential to predict drip loss non-destructively in a reasonable accuracy and the results could be visualised for identification and classification of beef muscles in a simple way. In addition to realize the difference in WHC within one sample, it was possible to accentuate the difference in samples having different drip loss values.     

Preliminary study on the use of near‐infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants

Near‐infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole‐wheat plant (Triticum aestivum L). Whole‐wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross‐validation (leave‐one‐out method). Coefficient of determination in calibration (R²_CAL) and the standard error of cross‐validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R²_CAL and SECV for N compared with fresh presentation (R²_CAL > 0.90, SECV < 0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried). Copyright © 2006 Society of Chemical Industry     

Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy

The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500 nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (R²) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (R²<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.     

Non‐destructive measurement of grapevine water potential using near infrared spectroscopy

Background and Aims: Near infrared (NIR) spectroscopy techniques are not only used for a variety of physical and chemical analyses in the food industry, but also in remote sensing studies as tools to predict plant water status. In this study, NIR spectroscopy was evaluated as a method to estimate water potential of grapevines. Methods and Results: Cabernet Sauvignon, Chardonnay and Shiraz leaves were scanned using an Integrated Spectronic (300–1100 nm) or an ASD FieldSpec^® 3 (Analytical Spectral Devices, Boulder, Colorado, USA) (350–1850 nm) spectrophotometer and then measured to obtain midday leaf water potential using a pressure chamber. On the same shoot, the leaf adjacent the one used for midday leaf water potential measurement was used to measure midday stem water potential. Calibrations were built and NIR showed good prediction ability (standard error in cross validation (SECV) <0.24 MPa) for stem water potential for each of the three grapevine varieties. The best calibration was obtained for the prediction of stem water potential in Shiraz (R = 0.92 and a SECV = 0.09 MPa). Conclusion: Differences in the NIR spectra were related to the leaf surface from which the spectra were collected, and this had an effect on the accuracy of the calibration results for water potential. We demonstrated that NIR can be used as a simple and rapid method to detect grapevine water status. Significance of the Study: Grapevine water potential can be measured using NIR spectroscopy. The advantages of this new approach are speed and low cost of analysis. It may be possible for NIR to be used as a non‐destructive, in‐field tool for irrigation scheduling.     

可见/近红外光谱技术无损检测新鲜鸡蛋蛋白质含量的研究

鸡蛋是一种重要的食品,蛋白质是鸡蛋的主要营养成分。本研究利用可见近红外反射光谱技术无损检测新鲜鸡蛋的蛋白质含量。使用光谱仪获取新鲜鸡蛋在400~1100 nm波段范围内的漫反射光谱;分别使用多元散射校正(MSC)法和一阶导数法(1-D)对反射光谱进行预处理;对反射光谱、MSC处理光谱和1-D光谱,使用逐步回归法判别法选择最优波长组合,建立多元线性回归模型,使用全交叉验证法验证模型。结果表明,可见/近红外反射光谱经过多元散射校正后,确定的10个最优波长(400、403.16、407.9、714.6、715、715.58、970.4、970.75、973和974.45 nm)组合建立模型的校正和验证结果最好:选定模型的校正结果为R=0.92,SEC=0.42%;验证结果为Rcv=0.89,SECV=0.47%。研究表明可见/近红外反射光谱技术可以较好的预测新鲜鸡蛋的蛋白质含量,本研究可为可见近红外光谱技术在鸡蛋营养成分的快速检测提供一定的理论基础。     

Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham

The capability of near infrared (NIR) spectroscopy was examined for the purposes of quality control of the traditional Slovenian dry-cured ham “Kraški pršut.” Predictive models were developed for moisture, salt, protein, non-protein nitrogen, intramuscular fat and free amino acids in biceps femoris muscle (n = 135). The models' quality was assessed using statistical parameters: coefficient of determination (R²) and standard error (se) of cross-validation (CV) and external validation (EV). Residual predictive deviation (RPD) was also assessed. Best results were obtained for salt content and salt percentage in moisture/dry matter (R_CV² > 0.90, RPD > 3.0), it was satisfactory for moisture, non-protein nitrogen, intramuscular fat and total free amino acids (R_CV² = 0.75–0.90, RPD = 2.0–3.0), while not so for protein content and proteolysis index (R_CV² = 0.65–0.75, RPD < 2.0). Calibrations for individual free amino acids yielded R_CV² from 0.40 to 0.90 and RPD from 1.3 to 2.9. Additional external validation of models on independent samples yielded comparable results. Based on the results, NIR spectroscopy can replace chemical methods in quality control of dry-cured ham.     

Development of a predictive model to determine potato flour content in potato-wheat blended powders using near-infrared spectroscopy

Potato is a good source of dietary energy and several micronutrients, and the development of staple foods using potato-wheat blended powder has received much attention recently in China. A rapid and accurate method for determining the potato flour content in potato staple foods would be valuable to market regulation efforts. We developed a predictive model for the potato flour content in potato-wheat blended powders based on near-infrared spectroscopy (NIRS) analysis. The correction of the near-infrared optical path was carried out to eliminate optical path differences using multiplicative scatter correction (MSC) and smoothing of the spectra using the Savitzky-Golay smoothing first-orderderivative (S-G-1^stD).The prediction model was developed based on partial least squares (PLS) combined with cross-validation(CV) within the full spectrum(850–1100 nm). The results showed that the optimal main factors of potato flour content in potato-wheat blended powder were 9, and the coefficient of determination of calibration (R²c) and standard error of cross-validation (SECV) of the prediction model reached 0.9997 and 0.51, respectively, indicating a good correlation. The repeatability standard deviation (SDr) and repeatability coefficient of variation of cross-validation (CVr) in validated samples were 0.246 and 0.967, respectively, which indicated that the prediction model had good repeatability. The bias-corrected standard error of prediction (SEP) and correlation coefficient of validation (R²P) were 0.69 and 0.9995, respectively, demonstrating good accuracy and stability. The results of this study demonstrated that this prediction model based on NIRS could determine the potato flour content in potato-wheat blended powders accurately and quickly.     

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.     

Methane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed

Milk fatty acids (MFA) are a proxy for the prediction of CH₄ emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH₄ production, (2) to predict CH₄ production based on 6 data sets differing in the number and type of MFA, and (3) to test whether additional inclusion of energy-corrected milk (ECM) yield or dry matter intake (DMI) as explanatory variables improves predictions. Twenty dairy cows were used. Four diets were used based on corn silage (CS) or grass silage (GS) without (L0) or with linseed (LS) supplementation. Ten cows were fed CS-L0 and CS-LS and the other 10 cows were fed GS-L0 and GS-LS in random order. In feeding wk 5 of each diet, CH₄ production (L/d) was measured in respiration chambers for 48 h and milk was analyzed for MFA concentrations by gas chromatography. Specific CH₄ prediction equations were obtained for L0-, LS-, GS-, and CS-based diets and for all 4 diets collectively and validated by an internal cross-validation. Models were developed containing either 43 identified MFA or a reduced set of 7 groups of biochemically related MFA plus C16:0 and C18:0. The CS and LS diets reduced CH₄ production compared with GS and L0 diets, respectively. Methane yield (L/kg of DMI) reduction by LS was higher with CS than GS diets. The concentrations of C18:1 trans and n-3 MFA differed among GS and CS diets. The LS diets resulted in a higher proportion of unsaturated MFA at the expense of saturated MFA. When using the data set of 43 individual MFA to predict CH₄ production (L/d), the cross-validation coefficient of determination (R²_CV) ranged from 0.47 to 0.92. When using groups of MFA variables, the R²_CV ranged from 0.31 to 0.84. The fit parameters of the latter models were improved by inclusion of ECM or DMI, but not when added to the data set of 43 MFA for all diets pooled. Models based on GS diets always had a lower prediction potential (R²_CV = 0.31 to 0.71) compared with data from CS diets (R²_CV = 0.56 to 0.92). Models based on LS diets produced lower prediction with data sets with reduced MFA variables (R²_CV = 0.62 to 0.68) compared with L0 diets (R²_CV = 0.67 to 0.80). The MFA C18:1 cis-9 and C24:0 and the monounsaturated FA occurred most often in models. In conclusion, models with a reduced number of MFA variables and ECM or DMI are suitable for CH₄ prediction, and CH₄ prediction equations based on diets containing linseed resulted in lower prediction accuracy.     

Soy Sauce Classification by Geographic Region Based on NIR Spectra and Chemometrics Pattern Recognition

Statistical and artificial neural network (ANN) pattern recognition techniques were applied to NIR spectra of 38 soy sauce samples collected from the northern/central, western, and southern regions in Japan and related to differences in food flavorings. Linear discriminant analysis (LDA) and ANN using factor scores calculated from NIR spectra showed more accurate differentiations than those based on the original spectra. In LDA, the correctly assigned ratio was 81.6%. Correct classification ratios shown by Partial least squares (PLS2) were 84.2% and by ANN 76.3% in the cross-validation test. The differentiations suggested that there are quality differences in soy sauce among the three regions in Japan.     

Exploring relationships between satiation,perceived satiety and plant-based snack food features

We aimed to identify relations between satiation and subsequent satiety for six plant-based foods (apple, avocado, banana, carrot, chick pea and macadamia) using a panel of ten healthy participants and a complete crossover randomised design. Food was served as a mid-morning snack ad libitum over 20 min until participants were comfortably full, and subsequent satiety was monitored for up to 180 min. Carrot and macadamia had significantly higher perceived fullness during eating, but also significantly lower perceived satiety per gram than other foods. Food energy factors had no strong relationship with perceived fullness, but were significantly positively correlated with satiety. Mastication number had significant effects on both perceived fullness (negative) and satiety (positive). This pilot study showed that plant food factors affecting perceived fullness during eating and subsequent satiety are different, and suggests how portion control through satiation for different plant-based snacks may influence both energy intake and subsequent satiety.     

Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows

Assessing the cheese-making properties (CMP) of milks with a rapid and cost-effective method is of particular interest for the Protected Designation of Origin cheese sector. The aims of this study were to evaluate the potential of mid-infrared (MIR) spectra to estimate coagulation and acidification properties, as well as curd yield (CY) traits of Montbéliarde cow milk. Samples from 250 cows were collected in 216 commercial herds in Franche-Comté with the objectives to maximize the genetic diversity as well as the variation in milk composition. All coagulation and CY traits showed high variability (10 to 43%). Reference analyses performed for soft (SC) and pressed cooked (PCC) cheese technology were matched with MIR spectra. Prediction models were built on 446 informative wavelengths not tainted by the water absorbance, using different approaches such as partial least squares (PLS), uninformative variable elimination PLS, random forest PLS, Bayes A, Bayes B, Bayes C, and Bayes RR. We assessed equation performances for a set of 20 CMP traits (coagulation: 5 for SC and 4 for PCC; acidification: 5 for SC and 3 for PCC; laboratory CY: 3) by comparing prediction accuracies based on cross-validation. Overall, variable selection before PLS did not significantly improve the performances of the PLS regression, the prediction differences between Bayesian methods were negligible, and PLS models always outperformed Bayesian models. This was likely a result of the prior use of informative wavelengths of the MIR spectra. The best accuracies were obtained for curd yields expressed in dry matter (CY_DM) or fresh (CY_FRESH) and for coagulation traits (curd firmness for PCC and SC) using the PLS regression. Prediction models of other CMP traits were moderately to poorly accurate. Whatever the prediction methodology, the best results were always obtained for CY traits, probably because these traits are closely related to milk composition. The CY_DM predictions showed coefficient of determination (R²) values up to 0.92 and 0.87, and RS_y,x values of 3 and 4% for PLS and Bayes regressions, respectively. Finally, we divided the data set into calibration (2/3) and validation (1/3) sets and developed prediction models in external validation using PLS regression only. In conclusion, we confirmed, in the validation set, an excellent prediction for CY_DM [R² = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CY_FRESH (R² = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R² ≥ 0.70, RPD ≥1.8), which enable quantitative prediction.     

The influence of oral lubrication on food intake: A proof-of-concept study

As overeating, overweight and obesity remain public health concerns, it is crucial to design satiety-enhancing foods that suppress appetite and lower snack intake. Existing research identifies oro-sensory targets to promote satiation and satiety, yet it remains unclear as to whether it is ‘chewing’ or ‘oral lubrication’ that might amplify satiation signals. In this study, techniques from experimental psychology, food material science and mechanical engineering have been combined to develop model foods to investigate the role of chewing and oral lubrication on food intake. Novel model gels, similar in pleasantness, were given as a preload then their effects on subjective appetite and intake of a salty snack were measured in a between-subjects design. Three mint flavoured hydrogels were engineered to vary in their texture (fracture stress) and lubrication (inverse of coefficient of friction), and a control group received mint tea. Results showed that snack intake was suppressed by 32% after eating the low chewing/high lubricating preload compared to the high chewing/low lubricating preload (p < 0.05). Hunger ratings decreased from t₁ to t₃ (p < 0.05), however differences between conditions were subtle and not significant. Thus, this proof-of-concept study demonstrates that manipulating oral lubrication is a promising new construct to reduce snack intake that merits future research in the oro-sensory satiety domain.     

Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening,winemaking, and aging of white and red wines

The feasibility of near infrared (NIR) spectroscopy for predicting reducing sugar content during grape ripening, winemaking, and aging was assessed. NIR calibration models were developed using a set of 146 samples scanned in a quartz flow cell with a 50 mm path length in the NIR region (800–1050 nm), in a fiber spectrometer system working in transmission mode. Principal component analysis (PCA), partial least squares (PLS), and multiple linear (MLR) regressions were used to interpret spectra and to develop calibrations for reducing sugar content in grape, must, and wine. The PLS model based on the full spectral range (800–1050 nm), yielded a determination coefficient (r²) of 0.98, a standard error of cross validation (SECV) of 13.62 g/l and a root mean square error of cross validation (RMSECV) of 13.58 g/l. The mathematical model was tested with independent validation samples (n = 48); the resulting values for r², the standard error of prediction (SEP) and the root mean square error of prediction (RMSEP) for the same parameter were 0.98, 10.84, and 12.20 g/l, respectively. The loading weights of latent variables from the PLS model were used to identify sensitive wavelengths. To assess their suitability, MLR models were built using these wavelengths. Wavelength significance was analyzed by ANOVA, and four wavelengths (909, 951, 961, and 975 nm) were selected, setting statistical significance at the 99% confidence level. The MLR model yielded acceptable results for r² (0.92), SEP (19.97 g/l) and RMSEP (20.51 g/l). The results suggest that NIR spectroscopy is a promising technique for predicting reducing sugar content during grape ripening, as well as during the fermentation and aging of white and red wines. Individual fingerprint wavelengths strongly associated with reducing sugar content could be used to enhance the efficacy of this simple, efficient and low-cost instrument.     

Enhanced NIR Calibration for Wort Fermentability Using Orthogonal Signal Correction

An enhanced method for the calibration of Near Infra Red (NIR) reflectance spectra to wort fermentability is proposed using a signal pre‐processing algorithm called orthogonal signal correction (OSC). Pre‐processing NIR spectra prior to partial least squares Project to Latent Structures (PLS) regression modelling is becoming commonplace in multivariate calibration. A set of twenty wort samples subjected to a replicated 2² factorial design with a centre point and nine production samples were used to construct multivariate prediction models. The experimental design factors were the mash tun saccharification temperature and time used to purposely provide a sample set with significant leverage in the fermentability responses. Calibration PLS models for both wort apparent degree of fermentation (ADF) and final attenuation apparent extract (Final AE) values with and without OSC corrected spectra were compared demonstrating significant improvements in prediction capability with the prior (Q² = 0.90 versus Q² = 0.28). The OSC algorithm removed almost 60% of the variance in the NIR spectra, which was independent or orthogonal to the fermentability measures. By cleaning up the spectra, the standard errors of prediction (SEP) for ADF and Final AE were improved by 50 and 90%, respectively, illustrating not only the enhancement in calibration but also the aptness for process control applications. Various model validation tests, including an external validation example and random response permutation, verify the validity of the models using OSC. Furthermore, interpretation of the important wavelengths related to wort fermentability is provided and demonstrates that some key wavelengths are related to both carbohydrate overtones as well as nitrogen functional groups. The application of OSC prior to developing calibration models with NIR demonstrates promising results for brewers interested in real time control of wort fermentability.     

Applicability of NIR spectroscopy to determine oil and other physicochemical parameters in Rosa mosqueta and Chilean hazelnut

Due to the increasing interest in certain components, specially the oil, from non-conventional seeds as Rosa mosqueta (Rosa rubiginosa) and Chilean hazelnut (Gevuina avellana), quick determinations of oil and other parameters were carried out by using near-infrared (NIR) spectroscopy. Moisture, oil, fiber (as acid detergent fiber) and protein from solid samples of the seeds as mentioned, along with those of soybean (Glycine max), already analyzed by NIR and for serving as control for the variability of the method, were studied. Sample interactions to NIR radiations were processed using the multivariate regression algorithm Partial Least Squared (PLS) to build a calibration model. Standard error of cross-validation (SECV) was used to estimate the prediction error. Moisture of Rosa mosqueta, Chilean hazelnut presscake and soybean meal (in the ranges 10–15, 10–15, 8–10%, respectively), acid detergent fiber (60–68, 12–16, 10–15%, respectively), oil (1–4, 14–20, 5–13%, respectively) and protein (1–5, 8–15, 27–45%, respectively) were previously determined by wet analysis using standard methods, so creating a library. The possibility to analyze parameters from very different oilseeds with an acceptable uncertainty was also established. Standard errors of cross-validation were between 1.25 and 2.99%, being the oil content the best predicted parameter.     

A Proof of Concept Study for the Parameters of Corn Grains Using Digital Images and a Multivariate Regression Model

In this method, a numerical matrix comprised of ten color scales (RGB, HSV, L, and rgb) as independent variables from digitalized images was used as a proof of concept for the prediction of the mass, apparent volume, and bulk density parameters of grains for quality control considering post-harvest purposes. The goal was to develop a high throughput multivariate regression model using partial least squares (PLS) combined with the information from color images to assess the raw product. The data set of external samples was successfully evaluated with standard error of cross-validation (SECV) values of 1.23 g (16.4–28.9), 2.03 cm³ (20.5–40.5), and 0.018 g cm^?3 (0.68–0.85) for the mass, apparent volume, and bulk density, respectively.     

Kernel partial least squares model for pectin content in peach using near-infrared spectroscopy

Pectin is a class of complex galacturonic acid-rich polysaccharides that are related to the texture of fruit and vegetables. The objective of this study was to develop, using near-infrared (NIR) spectroscopy, the best model for the determination of pectin content in peach fruit. A total of 100 samples divided into lossy and lossless samples were used to collect NIR raw spectra in the range of 1000–2500 nm. NIR absorption spectra were then obtained after pre-processing. Finally, four methods were used to establish lossy and lossless spectral models. The 10-fold cross-validation coefficient of determination R² of the lossy model was between 0.364 and 0.628, whereas that of the lossless model was between 0.187 and 0.288, indicating that the lossy model was better than the lossless model. Among all samples, the kernel partial least squares (KPLS) lossy model was better, with coefficient of determination R² = 0.628, root mean square error (RMSE) = 0.069 and mean absolute error (MAE) = 0.061. This is the first study to evaluate the prediction of peach pectin content using NIR spectroscopy, and the model can be used for rough screening.     

Application of Portable and Handheld Infrared Spectrometers for Determination of Sucrose Levels in Infant Cereals

Sucrose coating of breakfast cereals is used to enhance the flavor and attractiveness of the final product but there is a need for monitoring its levels to meet consumer health concerns associated with sugar consumption. Our objective was to evaluate the use of portable (mid-infrared, MIR) and handheld (near-infrared, NIR) systems for rapid, simple and reliable determination of sucrose content in breakfast cereal products. Cereal-based and sucrose-coated samples were provided by an Ohio snack food company. Samples were ground and spectra were collected using portable ATR-MIR (Cary 630) and handheld NIR (microPHAZIR) spectrometers. Reference sucrose levels were determined by high-performance liquid chromatography (HPLC). Partial least squares regression (PLSR) was used to develop calibration regression models for prediction of sucrose levels in breakfast cereals based on spectral data. Sucrose levels in uncoated (n?=?28) and coated (n?=?62) cereal samples were on average of 1.2?±?0.7 and 11.8?±?3.5 g/100 g, respectively. Similar calibration (n?=?85) model performances were obtained for determination of sucrose content by using the portable MIR and handheld NIR instruments with standard error of cross-validation (SECV) of 1.45 %. However, superior predictive ability was obtained with the portable MIR unit using a validation set (n?=?20, SEP?=?1.27 % and RPD?=?4.41). Regression models using NIR spectrum of the cereal through a polyethylene bag resulted in reduction of the model goodness of fit and RPD values. Results support the application of handheld NIR and portable MIR spectrometers for close-to-real-time analysis of sucrose levels in breakfast cereals providing simple, rapid and reliable prediction for quality assurance.     

基于近红外光谱技术测定稻谷水分含量研究

本文采用近红外光谱技术结合化学计量学方法,建立稻谷水分含量测定的快速分析方法。试验选取江苏省不同地区的两年内197份稻谷样品作为建模集样品,对其进行化学分析和图谱扫描处理,通过近红外化学计量学软件初步建立稻谷水分含量的预测模型。建模结果显示运用PLS（偏最小二乘法）建立的分析模型预测效果最优,决定系数（R2）高达0.9689,交互验证标准差（SECV）为0.3434,选取24个未知样品作为验证集样品,验证决定系数（R2）高达0.9806,预测标准差为0.0933。结果表明,近红外光谱技术可以用于稻谷水分含量的快速测定。