Prediction of sweetpotato starch physiochemical quality and pasting properties using near-infrared reflectance spectroscopy

A rapid predictive method based on near-infrared reflectance (NIR) spectroscopy (NIRS) was developed to measure sweetpotato starch physiochemical quality and pasting properties. The starch samples were scanned by NIRS and analyzed for quality properties by reference methods, respectively. Results of statistical modeling indicated that NIRS was reasonably accurate in predicting amylose content (AC), amylose percent (AP), total starch content (TSC), protein content (PRC), phosphorus content (PHC), solubility (SOL), swelling power (SP), average granule diameter (AGD), big granule percent (BGP), small granule percent (SGP), crystallinity (CRY), peak viscosity (PKV), hot paste viscosity (HPV), setback (SB), and pasting temperature (P_temp) with high coefficients of determination (RSQ = 0.85–0.92) and relatively low standard errors of prediction. The results showed that NIR analysis was sufficiently accurate and effective for rapid evaluation of starch physicochemical properties in sweetpotato. The NIR-based protocol developed in this study can be used for screening large number of starch samples in food enterprises and sweetpotato breeding programs.     

Prediction of pork quality using visible/near-infrared reflectance spectroscopy

Near-infrared spectroscopy is a rapid screening technique that may be used to determine meat quality traits. While several calibrations on meat quality parameters have been published, the accuracy and robustness of a calibration has rarely been validated with independent samples. In this study, in 207 loin muscles from three independent batches of pigs of different breeds drip loss, colour values, pH and intramuscular fat were determined. Calibrations were made from each combination of two batches and validated with the third batch. Validations of pH, intramuscular fat, drip loss, and L(?), a(?), and b(?) colour values had on average 1.27 times the accuracy of the calibration. Breed did not influence the accuracy of the calibration. Intramuscular fat can be determined with good accuracy. Muscle pH and colour values are reasonably well predicted. Drip loss can not be determined quantitatively with sufficient accuracy, but classification of quality groups is possible.     

Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS)

The aim of this study was to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) for predicting lamb meat fatty acid composition. We compared ground vs. intact non-ground meat samples to determine whether grinding and homogenisation of meat samples improved the performance of the predictions. We used 76 male lambs, of which 32 were pasture-fed and 44 stall-fed with concentrate and hay. The reflectance spectrum of Longissimus lumborum muscle was measured at wavelengths between 400 and 2500 nm. Predictions were better with ground than with intact muscle samples. NIRS accurately predicts several individual fatty acids (FA) (16:0, 18:0, 16:1 Δ9 cis, 17:1 Δ9 cis, 18:1 Δ9 cis, 18:1 Δ11 cis and 16:1 Δ9 trans) and several FA groups (total linear saturated FA, total branched saturated FA, total saturated FA, total cis monounsaturated FA (MUFA), total trans MUFA, total MUFA and total polyunsaturated PUFA). These results show the potential of NIRS as a rapid, and convenient tool to predict the major FA in lamb meat.     

Prediction of laboratory and in situ protein fractions in legume and grass silages using near-infrared reflectance spectroscopy

Legume and grass silage samples (n = 121) were collected from commercial forage testing laboratories (trial 1). Samples were dried at 55 degrees C for 48 h, ground, scanned on a near-infrared reflectance spectrophotometer, and analyzed for crude protein (CP), soluble CP, acid detergent fiber (ADF) CP, and neutral detergent fiber (NDF) CP by wet chemistry methods. Sixty samples were selected for calibration development, and the remaining samples were used for equation validation. Near-infrared reflectance spectroscopy accurately predicted the CP content of the silages (R2 = 0.96), but prediction of soluble CP, ADF CP, and NDF CP was markedly less accurate. The coefficients of determination and standard errors of calibration for CP, ADF CP, NDF CP (percentage of DM), and soluble CP (percentage of CP) were as follows (0.96 and 0.80, 0.77 and 0.24, 0.72 and 0.71, and 0.82 and 4.40). In a second study, legume and grass silage samples (n = 32) were dried at 55 degrees C and ground (2 mm). Duplicate dacron bags containing 5 g of silage were incubated in the ventral rumen of three ruminally cannulated cows for 0, 3, 6, 12, 24, 48, and 72 h. In situ protein fractions, including rapidly degraded protein, slowly degraded protein, undegradable protein, degradation rate, and rumen-undegradable protein, were determined. Original samples were reground (1 mm) and scanned. Previously defined near-infrared spectroscopy calibration procedures were conducted. Coefficients of determination for in situ CP fractions were R2 > 0.92 with the exception of degradation rate (R2 = 0.87). Data suggest that in situ protein fractions are better predicted by near-infrared reflectance spectroscopy than by laboratory protein fractions.     

Prediction of fat in intact cereal food products using near‐infrared reflectance spectroscopy

To evaluate the feasibility of an intact product approach to the near‐infrared (NIR) determination of fat content, a rapid acquisition spectrometer, with an InGaAs diode‐array detector and custom built sampling device, was used to obtain reflectance spectra (1100–1700 nm) of diverse cereal food products. Fat content reference data were obtained gravimetrically by extraction with petroleum ether (AOAC Method 945.16). Using spectral and reference data, partial least‐squares regression analysis was applied to calculate a NIR model (n = 89) to predict fat in intact cereal products; the model was adequate for rapid screening of samples, predicting the test samples (n = 44) with root mean square error of prediction (RMSEP) of 11.8 (range 1.4–204.8) g kg^?1 and multiple coefficient of determination of 0.98. Repeated repacking and rescanning of the samples did not appreciably improve model performance. The model was expanded to include samples with a broad range of particle sizes and moisture contents without reduction in prediction accuracy for the untreated samples. The regression coefficients for the models calculated indicated that spectral features at 1165, 1215 and 1395 nm, associated with CH stretching in fats, were the most critical for model development. Published in 2005 for SCI by John Wiley & Sons, Ltd.     

Prediction of the amino acid composition in brown rice using different sample status by near-infrared reflectance spectroscopy

In this study, 279 samples of brown rice grains and their flour, selected from a larger original population, were scanned by NIRSystem model 5000 mono-chromator in these two kinds of sample status for near-infrared reflectance spectroscopy (NIRS) analysis. Spectral pretreatment method 2,8,8,1 combined with SNV + D scatter correction was found suitable for developing calibration equations for amino acids. Equations for total amino acid content and for all individual amino acids, excluding cystine, methionine and tyrosine, were developed with this spectral pretreatment method. These equations had low SECV (0.010–0.063%) and SEP (0.011–0.066%); with high 1 − VR (0.878–0.960), R² (0.837–0.947) and SD/SEP (2.421–4.333). The results suggest that equations for the thirteen amino acids from the two sample categories can be directly used to estimate the amino acid composition in brown rice. This indicates once more that NIRS is a powerful technology that could be very useful for the determination of amino acid content in breeding programs that involve brown rice as well as for quality control in the food industry.     

无效变量消除法在油菜籽芥酸近红外无损速测中的应用

探索改善油菜籽芥酸近红外预测模型准确度与精密度的方法,利用无效变量消除法（UVE）,对135个油菜籽样品近红外光谱信号进行筛选,并利用筛选后的光谱对油菜籽芥酸含量进行偏最小二乘法交叉验证。结果表明,UVE法筛选变量后建立的芥酸校正模型对未知样品预测结果的准确度和速度显著优于全波长参与建立的芥酸校正模型。散射校正加一阶导数对光谱预处理,UVE法筛选变量,偏最小二乘法交叉验证建立的校正模型效果最好,其预测值与标准值的相关系数R达到0.92,交叉验证预测均方差为2.2。因此,用UVE进行波长选择后建立的近红外模型,能准确快速地对油菜籽芥酸含量进行定量分析。     

The effect of oak wood source, location of seasoning and coopering on the composition of volatile compounds in oak-matured wines

The effect of geographical origin and place of seasoning and coopering of oakwood on the concentration of 20 compounds extracted into, or formed in, Chardonnay, Cabernet Sauvignon and model wines during barrel maturation is described. The oak from which the barrels were fabricated was sourced from the Vosges and Limousin regions and the Tronçais forest in France, and from Ohio in the USA, and was seasoned in either Australia, France or the USA. Wines matured in Vosges oak barrels contained the highest concentrations of cis and trans- oak lactone and eugenol. The Limousin oak barrels imparted lower levels of cis- oak lactone and eugenol to the wines than did the other French oaks. However, the Limousin-oaked wines were richer in these compounds than the American-oaked wines. These results reflected the previously reported composition profiles of the oak-wood from which the barrels were fabricated. Oak seasoned and coopered in Australia generally imparted more cis- oak lactone, eugenol and the coopering products vanillin and furfural into the wines than did the oak seasoned in France or the USA. Although medium toast had been specified for all barrels, there was considerable variability in the concentration of compounds formed by coopering in the wines. Apparent random microbiological activity in the wines also contributed substantially to variation in wine composition. As a likely result of coopering and microbiological variability, few consistent origin or seasoning effects on the compounds resulting from coopering or microbiological action during oak maturation were observed.     

Predicting condensed tannin concentrations in Lotus uliginosus Schkuhr using near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to develop equations to predict condensed tannins (CT) in greater lotus (Lotus uliginosus Schkuhr) with a precision satisfactory for the screening of genotypes in a plant breeding programme. NIRS equations were developed using both partial least squares and stepwise multiple linear regression techniques, and a wide range of mathematical treatments of the log 1/reflectance NIRS data. In general, equations developed using partial least squares regression techniques had 10–15% lower standard errors than those developed using stepwise multiple linear regression, during both calibration and prediction. Standard errors of calibration ranged from 10 to 17 g kg⁻¹, standard errors of prediction from 12 to 17 g kg⁻¹. These errors equate to coefficients of variation in the order of 20% The use of NIRS to predict CT in greater lotus will allow more rapid evaluation and selection of genotypes than could be achieved using butan-1-ol/HCl hydrolysis. © 1997 SCI.     

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350–1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6–6) showed high de-noising ability with good information preservation. The first derivative of db6–6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R²) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.     

近红外光谱法测定高粱原花青素含量

利用近红外漫反射光谱技术,采用偏最小二乘法(PLS)探讨用近红外技术快速筛选高含量原花青素高粱的可行性.结果,内部交叉验证决定系数fR2)为0.8657,标准差(RMSECV)为0.277;外部验证决定系数(R2)为0.8919,标准差(RMSEP)为0.312.结果表明,用PLS建立的近红外模型能准确地筛选出高原花青素含量的高粱品种,为高粱的育种工作者提供了一种快速非破坏的筛选手段.     

Nondestructive determination of herbicide-resistant genetically modified soybean seeds using near-infrared reflectance spectroscopy

The objective of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) to differentiate the herbicide-resistant genetically modified (GM) and non-GM soybean seeds. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to classify soybeans with different genes into two groups: genetically modified organisms (GMO) and non-GMO. Calibrations were developed using PLSDA regression with cross-validation. Differences between GM and non-GM soybeans do exist, and excellent classification can be obtained after optimising spectral pretreatment. The PLSDA model using the second derivative pretreatment of the raw spectra had the best calibration and prediction abilities, with 97% accuracy. The results of the present study show that NIRS, together with chemometrics techniques, can be used to identify GM soybeans, thus circumventing time-consuming, costly and laborious chemical and sensory analyses.     

Effects of pre-fermentation enzyme maceration on extraction and colour stability in Pinot Noir wine

Effects of pre-fermentation enzyme maceration on anthocyanins, phenolics, colour and colour parameters were investigated in Pinot Noir ( Vitis vinifera L.) wines made from musts that were macerated for 12 hours at 20°C with a commercial enzyme preparation, Rohapect VR-C (Rohm, Darmstadt, Germany), with and without additions of SO₂. Anthocyanins and phenolics were determined by spectrophotometric and HPLC methods. Standard triplicate microvinifications were conducted in glass vessels. Extraction of anthocyanins was not increased by enzyme treatment. However, wines produced by enzyme maceration had increased visible colour intensity, colour density and polymeric pigment formation. By the end of fermentation, enzyme-treated wines had more polymeric pigment, and less monomeric anthocyanins than non-enzyme-treated wines. The wines were bottled, stored at 12°C and sampled at three, six, eight and 18 months after bottling. Over 18 months, differences between the treatments were maintained. The promotion of the formation of polymeric pigments in enzyme-treated wines continued after the wines were bottled, and the greater content of polymeric pigment led to greater colour density in the enzyme-treated wines. The promotion of polymeric pigment formation in wines by macerating enzyme treatment of the must may be due to the breakdown of protective polysaccharide-protein colloids by hemicellulase and protease activities in the enzyme preparation.     

Simultaneous estimation of amylose,resistant, and digestible starch in pea flour by visible and near-infrared reflectance spectroscopy

Due to its health benefits, resistant starch (RS) has received increasing attention from the public, and there is a need to develop methods to measure the amylose and RS concentration in pea (Pisum sativum L.) flour. The aim of this study was to develop a visible and near-infrared reflectance (vis–NIR) model for the simultaneous determination of amylose, RS, and digestible starch (DS) in pea flour. A total of 123 dry pea samples consisting of different pea varieties grown in different environments were collected, and ground to flour, and then the vis–NIR spectra were scanned. The amylose, RS, and DS contents of the pea flours were also measured by an enzymatic colorimetric assay. The spectra data were calibrated with the enzymatic colorimetric-assayed values. Results showed that amylose, RS, and DS in the pea flours can be simultaneously estimated using the vis–NIR spectra. Instead of using the full spectrum (300–2300 nm), we found the most efficient wave bands lying in the visible region between 370 and 560 nm and the NIR spectra in the range of 1600–1800 nm. Using the stepwise regression with backward elimination method, the multiple linear regression (MLR) models were developed from the most efficient wavelengths. The MLR models had the determination coefficients R² of 0.95, 0.76, 0.80, and 0.88 for amylose, RS, DS, and total starch, respectively. The correlation coefficients between model estimated and the enzymatic colorimetric assayed values were 0.97, 0.80, 0.85, and 0.93 for amylose, RS, DS, and total starch, respectively.     

The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle

Visible and near-infrared reflectance spectroscopy (VIS/NIRS) was used to predict colour on pork muscle samples. Colour values were measured on 44 pork muscles (n=44) using a Minolta digital camera to determine CIE L* (lightness), CIE a* (redness) and CIE b* (yellowness). Samples were scanned in the visible and near-infrared region of the spectra on a monochromator instrument (400–2500 nm) on both intact and homogenised presentation to the instrument. Two mathematical treatments (first and second derivative) and two scatter transformations to the spectra were explored, none and standard normal variate and detrend (SNV-D). Predictive (VIS/NIRS) calibrations were developed using a modified partial least-squares regression (mPLS) with internal cross validation. The highest coefficients of determination in calibration (R²_cal) and lower standard errors in cross validation (SECV) were found for both CIE L* and a*, in homogenised presentation, while poor calibration statistics were found on intact presentation.     

河西走廊新引‘黑比诺’不同营养系葡萄与葡萄酒品质特征差异研究

为了在河西走廊等具有相似环境特征的葡萄酒产区获得优质葡萄酒原料,以甘肃河西走廊民勤县新引酿酒葡萄‘黑比诺’PN VCR9、PN VCR18、PN VCR20、PN 375、PN 792等5个营养系为材料,分析不同营养系成熟期葡萄与葡萄酒的物理性状、基本化学指标和酚类物质含量的差异,并基于不同营养系葡萄果皮及葡萄酒的酚类物质进行主成分分析（PCA）。结果表明,5个‘黑比诺’营养系的果穗和果粒物理性状、果实基本理化指标及葡萄与葡萄酒中酚类物质含量存在明显差异（P＜0.05）。PCA结果表明,果实综合品质特征从高到低依次为PN VCR20、PN 375、PN 792、PN VCR9、PN VCR18,其中PN VCR20、PN 375和PN 792更适宜酿造风味较浓的葡萄酒,酿酒葡萄‘黑比诺’PN VCR9和PN VCR18更适宜酿造口感柔和型的葡萄酒。     

Technical note: a near-infrared reflectance spectroscopy technique to predict particle size of starch within corn silage

Starch particle size characteristics of 81 diverse corn silage samples, which included 27 combinations of hybrid, planting dates, and harvest dates subjected to 3 different degrees of kernel processing, were determined via vertical shaking through 9 screens with nominal square apertures of 19.0, 13.2, 9.5, 6.7, 4.75, 3.35, 2.36, 1.18, and 0.6 mm and a pan. Starch content of dry matter remaining on each screen and on the pan for each corn silage was determined, and geometric mean particle size (GMPS, μm), starch particles(SP)/g, starch surface area (SSA, cm²/g), kernel processing score (KPS), % starch < 4.75 mm, and the percentage of total starch remaining on each screen of the vertical shakers were calculated. Near-infrared reflectance spectra were obtained by scanning 3 types of samples: 1 mm of dried ground corn silage; whole undried, unground corn silage; and undried, unground corn silage that passed through a 19-mm screen. Calibrations to predict GMPS, SP, SSA, and KPS characteristics of corn silage starch were attempted from each spectral origin. Calibrations to predict GMPS, SP, SSA, KPS, and the percentage of total starch retained on screens of the vertical shaker was unattainable (R² < 0.45) using spectra obtained from 1 mm of dried ground corn silage or whole undried, unground corn silage. However, reasonable near-infrared reflectance spectra equations (R² > 0.81) for GMPS, SSA, and KPS were attained using spectra from undried, unground corn silage that passed through a 19-mm screen. This technique holds promise as a rapid and efficient method to determine particle size characteristics of starch within corn silage.     

Effect of mechanical leaf removal and its timing on flavan‐3‐ol composition and concentrations in Vitis vinifera L. cv. Pinot Noir wine

Background and Aims: Mechanical leaf removal of Pinot Noir vines was carried out in a commercial vineyard in Waipara, New Zealand in the 2007–2008 and 2008–2009 growing seasons. The aim was to investigate the effects of different timings on flavan‐3‐ol composition and concentrations in Pinot Noir wine produced from the treatments. Methods and Results: Treatments were leaf removal from the fruiting zone 7 days after flowering, 30 days after flowering and at veraison (by visual assessment), and no leaf removal (control). Proanthocyanidin concentrations in berries by the methylcellulose precipitation assay at harvest showed no difference between treatments, although the LR7 wine had the highest methylcellulose precipitable tannin concentration in 2007–2008, the LR30 wine had the highest concentration in 2008–2009 and the NLR wine had the lowest in both years; the 2009 wines had increased concentrations compared to 2008. Greatest concentrations of flavan‐3‐ol monomers by reverse phase high performance liquid chromatography were found in LR7 wines. An increase in the ratio of 2,3‐trans to 2,3‐cis flavan‐3‐ols was observed with earlier leaf removal. The mean degree of polymerisation (mDP) by acid catalysis in the presence of phloroglucinol showed no statistical difference between wines. Conclusions: Early timing of mechanical leaf removal increases proanthocyanidin concentration, but has no influence on the mDP. Increased severity of defoliation and/or higher alcohol levels in the 2008–2009 growing season were responsible for the differences in total tannin concentration compared to the previous year. Significance of the Study: The timing of mechanical leaf removal can influence wine flavan‐3‐ol concentrations.     

Prediction of fatty acid composition of fresh and freeze-dried cheeses by visible–near-infrared reflectance spectroscopy

Visible–near-infrared reflectance spectroscopy was used to predict the fatty acid (FA) composition of 445 cows’ and goats’ fresh and freeze-dried cheeses. The FA composition of samples was analysed by reference method. Samples were scanned (400–2500 nm) and predictive equations were developed using modified partial least squares with both cross-validation and external validation. The coefficient of determination in external validation (R²V) and residual predictive deviation in the validation set (RPDV) were good for total saturated (0.89, 2.84), monounsaturated (0.90, 2.90), polyunsaturated (0.88, 2.54), trans (0.92, 3.24) FA, butyric (0.84, 2.43), capric (0.92, 2.96), myristic (0.89, 2.94), palmitic (0.91, 3.32), vaccenic (0.88, 2.63), and rumenic (0.90, 2.70) acids in fresh cheeses. Approximate or poor predictions were obtained for caproic, caprylic, lauric, stearic, oleic, linoleic, and linolenic acids (R²V0.80; RPDV2.08). The quantification was significantly more accurate for caprilic, capric, and linoleic acids with freeze-dried cheeses compared with fresh cheeses.