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 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.     

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.     

The use of near‐infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants

Near‐infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modified partial least squares (MPLS) regression was applied to scatter‐corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coefficients of determination (R²) and standard errors of cross‐validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg⁻¹ on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice. © 2000 Society of Chemical Industry     

Determination of dry matter and crude protein contents of undried forages by near‐infrared reflectance spectroscopy

Near‐infrared reflectance spectroscopy (NIRS) was used to predict the dry matter (DM) and crude protein (CP) contents of untreated forage samples. Four hundred forage samples were analysed in reflectance mode. Two mathematical treatments based on the order of derivative of log(1/R), the gap in data points and the numbers of data points used in the first and second smoothings were applied. Predictive equations were developed using modified partial least squares (MPLS) with internal cross‐validation. The coefficient of determination of calibration and the standard error of cross‐validation (SECV, in parentheses) for DM were 0.92 (12.4), 0.92 (12.6) and 0.93 (11.7) for the two treatments and log(1/R) respectively on a g kg^?1 fresh weight basis. For CP the NIRS calibration statistics yielded and SECV (in parentheses) values of 0.85 (19.8), 0.85 (19.5) and 0.87 (18.1) for the two treatments and log(1/R) respectively on a g kg^?1 fresh weight basis. It was concluded that NIRS is a suitable method to predict the dry matter and crude protein contents of fresh forage without sample preparation. © 2002 Society of Chemical Industry     

Evaluating NIR instruments for quantitative and qualitative assessment of intact apple quality

BACKGROUND: In recent years, near‐infrared reflectance (NIR) instruments have undergone radical changes, becoming much more versatile, more portable, cheaper and better adapted to hostile working areas. In this study, three commercially available spectrophotometers were evaluated for the determination of quality (soluble solid content, firmness and shelf‐life) in intact apples. The three instruments used, which differ primarily in terms of measurement principle and wavelength range, were a scanning monochromator (SM) with a range of 400–2500 nm, a combination of diode array and scanning monochromator (DASM) with a range of 350–2500 nm and a diode array (DA) with a range of 900–1700 nm. RESULTS: A total of 334 apples (Malus domestica Borkh.), cvs Fuji and Golden Delicious, were used to build calibration models in different spectral regions and using various spectral signal pretreatments. The three NIR instruments evaluated provided good precision for soluble solid content, with r² values between 0.90 and 0.94 and standard error of cross‐validation (SECV) values ranging from 0.51 to 0.68°Brix; however, firmness measurements were less precise in all three cases (r² = 0.52–0.57, SECV = 8.28–8.83 N). The performance of the three instruments in classifying apples by shelf‐storage duration (0, 8 and 14 days) was studied using partial least squares discriminant analysis to develop classification models. A total of 86.1% of samples from the mixed‐cultivar group were correctly assigned, compared with 86.6% of samples from single‐cultivar groups. CONCLUSION: The results obtained suggest that, in general, the three NIR instruments tested provided a similar level of accuracy for the measurement of soluble solid content, firmness and shelf‐life, being slightly better the prediction models developed with the DASM spectrophotometer. Copyright © 2009 Society of Chemical 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.     

Protein content determination in Brassica oleracea species using FT‐NIR technique and PLS regression

Six fresh and one frozen vegetable cultivar groups possessing remarkably different morphology from the same Brassica oleracea species, including broccoli, Brussels sprouts, curly cabbage, white cabbage, red cabbage, cauliflower and white kohlrabi, were chosen to set up a Fourier transform near‐infrared spectroscopy (FT‐NIR)‐based method for the quantification of protein content. Sample preparation was based on lyophilisation and homogenisation. Calibration was set up with the help of the Kjeldahl method for the quantification of protein content in the range of 12.9–32.5 m/m%. Calibration model was developed using the spectral regions 1136–1334 and 1639–1836 nm, with partial least squares regression. This model was checked by cross‐validation. The performance of the final FT‐NIR estimation model was evaluated by root mean square of cross‐validation, root‐mean‐square error of estimation and the determination coefficient (R²). The final estimation function for the protein determination was characterised with the predictive error of 0.76 m/m% and R² value of 98.81.     

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 (R²_CV) 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 (R² _CV 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.     

Potential use of visible and near infrared reflectance spectroscopy for the estimation of nitrogen fractions in forages harvested from permanent meadows

One‐hundred and ninety‐two herbage samples from permanent meadows located in the mountains of León (NW Spain) were analyzed for total nitrogen (total N), nitrogen in trichloroacetic acid precipitated matter (TCAN), borate–phosphate buffer insoluble nitrogen (BPBN), neutral‐detergent insoluble nitrogen (NDIN) and acid‐detergent insoluble nitrogen (ADIN). These data were used to calculate the partition of nitrogen fractions proposed by the Cornell Net Carbohydrate and Protein System (CNCPS): A (total N ? TCAN), B₁ (TCAN ? BPBN), B₂ (BPBN ? NDIN), B₃ (NDIN ? ADIN) and C (ADIN). Near‐infrared reflectance spectroscopy (NIRS) technology accurately predicted total N, TCAN and NDIN, as judged by coefficient of determination (R²) and ratio performance deviation (RPD) values greater than 0.90 and 2.5, respectively. The rest of the insoluble N fractions (BPBN and ADIN) were predicted with less accuracy by NIRS. Estimations of CNCPS N fractions (A, B₁, B₂, B₃ and C) using visible–NIR spectra of forage samples did not allow accurate predictions (R² < 0.90; RPD < 2.5). Copyright © 2005 Society of Chemical Industry     

Application of Electronic Nose for Measuring Total Volatile Basic Nitrogen and Total Viable Counts in Packaged Pork During Refrigerated Storage

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB‐N) in pork using an electronic nose (E‐nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O₂/40% CO₂/20% N₂). Principal component analysis (PCA) was used to analyze the E‐nose signals, and the results showed that the relationships between the freshness of chilled pork and E‐nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB‐N to E‐nose signals. High F and R² values were obtained in the MLR output of TVB‐N (F = 32.1, 21.6, and 24.2 for PP [R² = 0.93], VP [R² = 0.94], and MAP [R² = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R² = 0.98], VP [R² = 0.89], and MAP [R² = 0.85], respectively). The results of this study suggest that it is possible to use the E‐nose technology to predict TVB‐N and TVC for assessing the freshness of chilled pork during storage.     

WAVELENGTH SELECTION FOR PREDICTING PHYSICOCHEMICAL PROPERTIES OF APPLE FRUIT BASED ON NEAR-INFRARED SPECTROSCOPY

ABSTRACT

Instrumental evaluation tools for fruit quality monitoring are important in the production and postharvest processes as well as in marketing. In the present study, near‐infrared spectroscopy (600–1,100 nm) was applied to study the correlation with fruit soluble solid content (SSC ), fruit flesh firmness and water content of apples (cv. “Fuji”). Genetic algorithm and correlation coefficient (r) method were used to select the most sensitive wavelength combinations, and partial least squares regression analysis was applied to calibrate fruit quality parameter. The validation of models based on the most sensitive wavelengths gave good predictions with an r value of 0.94 and a standard error of cross validation (SECV) of 0.85°Brix for SSC; r = 0.89 and SECV = 7.54 N/cm² for firmness; and r = 0.96 and SECV = 0.92% for water content. The reduced data set of sensitive wavelengths were found feasible for predicting internal fruit quality.

PRACTICAL APPLICATIONS

Soluble solid content, firmness and water content are important quality attributes of apples. A nondestructive measurement technique will be valuable for monitoring and sorting apple fruit so that high quality, uniform fresh products can be delivered to the marketplace. In the present study, fruit analyses using the entire near‐infrared fruit spectra or a reduced data set of sensitive wavelengths were compared. The results demonstrate that the selected combinations of sensitive wavelengths were feasible for measuring apple quality properties. The recent research findings provide researchers and instrumentation engineers with information on the performance of different methods to select appropriate wavelengths for reducing the amount of data, e.g., in developing portable or online sensing systems.

     

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

PREDICTION OF TEXTURE IN GREEN ASPARAGUS BY NEAR INFRARED SPECTROSCOPY (NIRS)

NIR spectroscopy was used to estimate three textural parameters of green asparagus: maximum cutting force, energy and toughness. An Instron 1140 Texturometer provided reference data. A total of 199 samples from two asparagus varieties (Taxara and UC‐157) were used to obtain the calibration models between the reference data and the NIR spectral data. Standard errors of cross validation (SECV) and r² were (5.73, 0.84) for maximum cutting force, (0.58, 0.66) for toughness, and (0.04, 0.85) for cutting energy. The mathematical models developed as calibration models were tested using independent validation samples (n =20); the resulting standard errors of prediction (SEP) and r² for the same parameters were (6.73, 0.82), (0.61, 0.57) and (0.04, 0.89), respectively. For toughness, substantially improved r² (0.85) and SEP (0.36) when four samples exhibiting large residual values were removed. The results indicated that NIRS could accurately predict texture parameters of green asparagus.     

Mitochondrial DNA Fragmentation as a Molecular Tool to Monitor Thermal Processing of Plant‐Derived,Low‐Acid Foods,and Biomaterials

Cycle threshold (Ct) increase, quantifying plant‐derived DNA fragmentation, was evaluated for its utility as a time‐temperature integrator. This novel approach to monitoring thermal processing of fresh, plant‐based foods represents a paradigm shift. Instead of using quantitative polymerase chain reaction (qPCR) to detect pathogens, identify adulterants, or authenticate ingredients, this rapid technique was used to quantify the fragmentation of an intrinsic plant mitochondrial DNA (mtDNA) gene over time‐temperature treatments. Universal primers were developed which amplified a mitochondrial gene common to plants (atp1). These consensus primers produced a robust qPCR signal in 10 vegetables, 6 fruits, 3 types of nuts, and a biofuel precursor. Using sweet potato (Ipomoea batatas) puree as a model low‐acid product and simple linear regression, Ct value was highly correlated to time‐temperature treatment (R² = 0.87); the logarithmic reduction (log CFU/mL) of the spore‐forming Clostridium botulinum surrogate, Geobacillus stearothermophilus (R² = 0.87); and cumulative F‐value (min) in a canned retort process (R² = 0.88), all comparisons conducted at 121 °C. D₁₂₁ and z‐values were determined for G. stearothermophilus ATCC 7953 and were 2.71 min and 11.0 °C, respectively. D₁₂₁ and z‐values for a 174‐bp universal plant amplicon were 11.3 min and 9.17 °C, respectively, for mtDNA from sweet potato puree. We present these data as proof‐of‐concept for a molecular tool that can be used as a rapid, presumptive method for monitoring thermal processing in low‐acid plant products.     

The effect of maturity and tissue on the ability of mid infrared spectroscopy to predict the geographical origin of banana (Musa Cavendish)

The objective of this study was to evaluate the effect of sample presentation (tissue type) and maturity (ripe and unripe) on the classification of banana (Musa Cavendish) samples sourced from two different geographical regions and analysed using mid infrared (MIR) spectroscopy. The coefficient of determination (R²) and the standard error of cross-validation (SECV) obtained using partial least squares discriminant analysis were 0.83 (0.33), 0.75 (0.25) and 0.94 (0.19) for the prediction of maturity, geographical origin and tissue type, respectively. No effect of either of type of tissue (e.g. pulp or peel) or maturity was observed. The results of this study demonstrated that MIR spectroscopy might be used to classify the origin of the banana samples at different degrees of ripeness. However, one of the limitations of this study is on the number of samples analysed and further validation must be recommended using samples from other sources, regions and harvest seasons.     

Possibilities of using near infrared reflectance/transmittance spectroscopy for determination of polymeric protein in wheat

Possibilities of using near‐infrared reflectance and near‐infrared transmittance (NIR/NIT) spectroscopic techniques for detecting differences in amount and size distribution of polymeric proteins in wheat were investigated. To evaluate whether differences in polymeric protein due to genetic or environmental variations were detectable by NIR/NIT techniques, wheat materials of different background were used. Size‐exclusion high‐performance liquid chromatography was applied to detect variation in polymeric protein. Partial least squares regression gave high R² values between many protein parameters and NIR/NIT spectra (particularly second‐derivative spectra of NIR 1100–2500 nm region) of flours, while no such relationship was found for whole wheat grains. Most and highest correlations were found for total amount of extractable and unextractable proteins and monomer/polymer protein ratio. Some positive relationships were found between percentage of total unextractable polymeric protein in the total polymeric protein and percentage of large unextractable polymeric protein in the total large polymeric protein and NIR/NIT spectra. Thus, it was possible to detect differences in polymeric proteins with NIR/NIT techniques. The highest amount of positive correlations between NIR/NIT spectra and protein parameters was found to be due to environmental influences. Some correlations were found for breeding lines with a broad variation in gluten strength and polymeric protein composition, while a more homogeneous sample showed less correlation. Thereby, detection of variation in amount and size distribution of polymeric protein due to cultivar differences with NIR/NIT methods might be difficult. Copyright © 2007 Society of Chemical Industry     

Rapid detection model of Bacillus subtilis in solid-state fermentation of rapeseed meal

The main objective of this research was to monitor variety in the content of Bacillus subtilis 10160 during solid-state fermentation (SSF) of rapeseed meal by near infrared (NIR) spectroscopy and chemometrics. Observations showed the coefficient of determination (R²) value was 0.9401 and root-mean-square deviation was 0.639 log (CFU/g) for viable cell content by synergies between four intervals 5,203.003–5,600.267, 5,604.124–6,001.388, 6,807.487–7,204.751 and 8,411.972–8,809.235 cm⁻¹. The determination coefficient of prediction (R_p²) and root-mean-square deviation of prediction in an external validation set could reach 0.9532 and 0.628 log (CFU/g) by the viable B. subtilis model. These findings suggest that rapid detection of B. subtilis in SSF is achieved by the combination of synergy interval partial least squares and NIR spectroscopy.     

A Feasibility Study on Monitoring Residual Sugar and Alcohol Strength in Kiwi Wine Fermentation Using a Fiber‐Optic FT‐NIR Spectrometry and PLS Regression

This work aims to investigate the potential of fiber‐optic Fourier transform‐near‐infrared (FT‐NIR) spectrometry associated with chemometric analysis, which will be applied to monitor time‐related changes in residual sugar and alcohol strength during kiwi wine fermentation. NIR calibration models for residual sugar and alcohol strength during kiwi wine fermentation were established on the FT‐NIR spectra of 98 samples scanned in a fiber‐optic FT‐NIR spectrometer, and partial least squares regression method. The results showed that R² and root mean square error of cross‐validation could achieve 0.982 and 3.81 g/L for residual sugar, and 0.984 and 0.34% for alcohol strength, respectively. Furthermore, crucial process information on kiwi must and wine fermentations provided by fiber‐optic FT‐NIR spectrometry was found to agree with those obtained from traditional chemical methods, and therefore this fiber‐optic FT‐NIR spectrometry can be applied as an effective and suitable alternative for analyses and monitoring of those processes. The overall results suggested that fiber‐optic FT‐NIR spectrometry is a promising tool for monitoring and controlling the kiwi wine fermentation process.     

Technical note: Near infrared reflectance spectroscopy to predict fecal indigestible neutral detergent fiber for dairy cows

In vitro and in situ procedures performed to estimate indigestible neutral detergent fiber (iNDF) in forage or fecal samples are time consuming, costly, and limited by intrinsic factors. In contrast, near infrared reflectance spectroscopy (NIRS) has become widely recognized as a valuable tool for accurately determining chemical composition and digestibility parameters of forages. The aim of this study was to build NIRS calibrations and equations for fecal iNDF. In total, 1,281 fecal samples were collected to build a calibration data set, but only 301 were used to develop equations. Once dried, samples were ground and chemically analyzed for crude protein, ash, amylase and sodium sulfite–treated NDF corrected for ash residue (aNDFom), acid detergent fiber, acid detergent lignin, and in vitro digestion at 240 h to estimate iNDF (uNDF240). Each fecal sample was scanned using a NIRSystem 6500 instrument (Perstorp Analytical Inc., Silver Spring, MD). Spectra selection was performed, resulting in 301 sample spectra used to develop regression equations with good accuracy and low standard error of prediction. The standard error of calibration (SEC), cross validation (SECV), and coefficients of determination for calibration (R²) and for cross validation (1 ? VR, where VR = variance ratio) were used to evaluate calibration and validation results. Moreover, the ratio performance deviation (RPD) and ratio of the range of the original data to SECV (range/SECV; range error ratio, RER) were also used to evaluate calibration and equation performance. Calibration data obtained on fiber fractions aNDFom (R² = 0.92, 1 ? VR = 0.87, SEC = 1.48, SECV = 1.89, RPD = 2.80, and RER = 20.19), uNDF240 (R² = 0.92, 1 ? VR = 0.86, SEC = 1.65, SECV = 2.24, RPD = 2.57, and RER = 14.30), and in vitro rumen aNDFom digestibility at 240 h (R² = 0.90, 1 ? VR = 0.85, SEC = 2.68, SECV = 3.43, RPD = 2.53, and RER = 14.0) indicated the predictive equations had good predictive value.