Application Of Near Infrared Reflectance Spectroscopy Research Articles

Application of Near-infrared reflectance spectroscopy in the diagnosis of varicocele in children and adolescents

Varicocele is a venous anomaly characterized by the enlargement of the plexus of veins in the testicle. In pediatric cases, indications for surgical intervention in varicocele include palpable dilatation of the enlargement of the plexus veins, intermittent pain in the ipsilateral testicle, and a 20% reduction in size of the ipsilateral testicle compared to the contralateral healthy one. The limitations of evaluating these criteria and the need to assess semen parameters prompt clinicians to explore new diagnostic methods for timely surgical interventions in adolescents. One such promising study involves Near-infrared reflectance spectroscopy (NIRS) in the Near-infrared range. The aim of the research is to evaluate the diagnostic capabilities of NIRS in children with varicocele to determine the indications for surgical intervention. The study included 65 boys aged 11 to 17 years with left-sided grade III varicocele (main group) and 29 healthy boys as a control group. Monitoring of regional oxygen saturation (rSO2) of testicular tissues was conducted using optical spectroscopy. NIRS examination of testicular tissues in both study and control groups showed statistically homogeneous data for regional oxygen saturation in the healthy (right) testicle. However, the NIRS study revealed significantly lower oxygen saturation (rSO2) in the testicular tissue of the left testicle in patients with varicocele compared to the control group. The diagnostic significance of the difference in bilateral NIRS monitoring Δ rSO2 >13% reliably characterized the presence of testicular hypotrophy by more than 20% compared to the healthy contralateral side (AUC=0.64, 95% CI (0.505-0.775), sensitivity – 79%, specificity – 50%, p=0.042). Δ rSO2 >11% values indicated disturbances in spermogram related to oxidative stress (AUC=0.702, 95% DI (0.51-0.88), sensitivity – 82%, specificity – 61%, p=0.031). Therefore, NIRS study data have potential valuable information for the diagnosis and selection of treatment for varicocele in children. The research utilizing Near-infrared Reflectance Spectroscopy revealed a significant potential of this method for diagnosing and assessing the impact of varicocele on the microcirculation of testicular tissue in children. The diagnostic significance of the difference in bilateral monitoring using this non-invasive technique in determining the state of testicles and the possibility of addressing the question of the necessity of surgical intervention is established.

Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves.

Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves.

Application of Near-Infrared Reflectance Spectroscopy for Predicting Damage Severity in a Diverse Panel of Tectona grandis Caused by Ceratocystis fimbriata.

Tectona grandis Linn., also known as teak, is a highly valued species with adaptability to a wide range of climatic conditions and high tolerance to soil variations, making it an attractive option for both commercial and conservation purposes. In this sense, the classification of cultivated teak genotypes is crucial for both breeding programs and conservation efforts. This study examined the relationship between traits related to damage in the stem of teak plants caused by Ceratocystis fimbriata (a soil-borne pathogen that negatively impacts the productivity of teak plantations) and the spectral reflectance of 110 diverse clones, using near-infrared spectroscopy (NIRS) data and partial least squares regression (PLSR) analysis. Cross-validation models had R2 = 0.894 (ratio of standard error of prediction to standard deviation: RPD = 3.1), R2 = 0.883 (RPD = 2.7), and R2 = 0.893 (RPD = 2.8) for predicting stem lesion area, lesion length, and severity of infection, respectively. Teak genotypes (clones) can benefit from the creation of a calibration model utilizing NIRS-generated data paired with PLSR, which can effectively screen the magnitude of damage caused by the fungus. Overall, while the study provides valuable information for teak breeding and conservation efforts, a long-term perspective would be essential to evaluate the sustainability of teak genotypes over various growth stages and under continuous pathogen pressure.

Monitoring the concentrations of Cd, Cu, Pb, Ni, Cr, Zn, Mn and Fe in cultivated Haplic Luvisol soils using near-infrared reflectance spectroscopy and chemometrics

This study illustrates the successful application of near-infrared reflectance spectroscopy extended with chemometric modeling to profile Cd, Cu, Pb, Ni, Cr, Zn, Mn, and Fe in cultivated and fertilized Haplic Luvisol soils. The partial least-squares regression (PLSR) models were built to predict the elements present in the soil samples at very low contents. A total of 234 soil samples were investigated, and their reflectance spectra were recorded in the spectral range of 1100–2500 nm. The optimal spectral preprocessing was selected among 56 different scenarios considering the root mean squared error of prediction (RMSEP). The partial robust M-regression method (PRM) was used to handle the outlying samples. The most promising models were obtained for estimating the amount of Cu (using PRM) and Pb (using the classic PLS), leading to RMSEP expressed as a percentage of the response range, equal to 9.63% and 11.5%, respectively. The respective coefficients of determination for validation samples were equal to 0.86 and 0.58, respectively. Assuming similar variability of model residuals for the model and test set samples, coefficients of determination for validation samples were 0.94 and 0.89, respectively. Moreover, the favorable PLS models were also built for Zn, Mn, and Fe with coefficients of determinations equal to 0.87, 0.87, and 0.79.

Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination

The application of near-infrared reflectance spectroscopy (NIRS) and multivariate analysis for determining the seed germination rate of corn genotypes was assessed. Seed samples about 90 gr belong to commercial and local corn varieties at various ages were scanned with FT-NIRS on the reflectance mode from 1000 to 2500 nm wavelength. Filter paper technique showed the seed germination rates varied between 18-100% depending on the genotypes after 7 days at ±25°C. Partial least squares regression (PLSR) was applied to the reference values corresponding to the spectra. The best statistical results obtained from the pre-treatment combinations of Smooth Savitzky-Golay 9 Points (sg9), MSC full and normalization to unit length (nle). The regression coefficient of calibration (R2C) and prediction (R2P) of the created NIRS calibration via chemometric software NIRCal are realized 0.97 and 0.98 respectively for the property of corn germination rate. The standard error of both calibration (SEC) and prediction (SEP) were almost overlapping (4.17%, 4.61% respectively). The prediction accuracy of the final NIRS model was quite reasonable with the acceptable root mean standard error of prediction (RMSEP) as 8.88%. According to the residual predictive deviation (RPD) index (4.18), the accuracy of the NIRS model regarded as in the best category. Therefore, the NIRS model developed here is sufficient to predict the corn seed germination rate very fast and non-destructively without using any regents.

The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

Mineralogical analysis of ferralitic soils in Madagascar using NIR spectroscopy

The applicability of near-infrared reflectance spectroscopy (NIRS) as tool for estimating mineral soil constituents was assessed over a wide range of highly weathered soils in Madagascar. The predictions were based on two types of methods, chemometric methods using multivariate calibration models from partial least squares (PLS) regressions, and the use of spectral signatures of specific minerals. The predictions of mineralogical properties of soils using chemometric methods were poor, except for the quantification of iron oxides extracted with citrate-bicarbonate dithionite (CBD) (R2cv=0.80). Soil minerals (kaolinite, gibbsite, goethite and hematite) were also estimated by NIRS but with less accuracy (R2cv=0.50–0.80). The predictions of kaolinite and gibbsite contents were improved by the use of the peak intensity of the first derivative spectra, situated at around 2205nm for kaolinite and 2265nm for gibbsite. The results indicate that NIRS can be used as a rapid analytical technique to simultaneously estimate the main minerals of highly weathered ferralitic soils with acceptable accuracy.

Application of near-infrared reflectance spectroscopy for predicting carotenoid content in summer squash fruit

The potential of near-infrared reflectance spectroscopy (NIRS) for predicting total carotenoid, lutein and β-carotene contents in skin and flesh of Cucurbita pepo fruits was assessed. The carotenoid contents were performed by HPLC, and were regressed against different spectral transformations by modified partial least square (PLSm) regression. Coefficients of determination in the external validation varied from 0.81 to 0.96, which characterize those equations as having from good to excellent quantitative information. The standard deviation (SD) to standard error of prediction ratio (RPD) and range to standard error of prediction ratio (RER) were variable for the different fruit part and compounds, and showed values that were characteristic of equations suitable for screening purposes. PLSm loading plots corresponding to the first terms of the equations showed that effects of the C–H group of starch and lipids, O–H group of water, as well as protein and chlorophyll, were most important in modeling prediction equations. The use of NIRS represents an important breakthrough in breeding for improved nutritional quality of summer squash fruit.

Study temperature influence in the application of near- infrared reflectance spectroscopy (NIRS) to estimate amylose content of rice

In the application of near-infrared reflectance spectroscopy (NIRS) to estimate amylose content of rice, analysis was performed based on the relationship between absorbance and amylose content. But absorbance is sensitive to environmental temperature. In this study, characteristics of rice spectra collected at different temperatures were studied by comparing the average spectra and standard deviation spectra of rice with same amylose content at the temperature of 5, 10, 15 and 20°C. The results showed that spectra collected at different temperatures had significant difference in absorbance, and spectra acquired at different temperatures had different stabilities. When the environment temperature was 15°C, stability of spectra was better than when the temperature was 5, 10 and 20°C within the range consisting of abundant information. Accuracies of four linear models on calibration set acquired at four temperatures built by partial least square (PLS) were investigated by using validation sets got at four different temperatures to perform cross validation. The results showed that the relation between spectra and amylose content was not linear in the result of temperature’s influence, and non-linear model methodology least square support vector machine (LS-SVM) method was used to establish calibration model on the calibration set acquired at 15°C. The validation results indicated that the non-linear model could predict the amylose content of samples collected at 15°C with high accuracy, and the root mean square error of prediction was 0.62. Meanwhile, non-linear model showed good prediction ability for samples whose spectra were collected at other temperatures. Key words: Amylose content, near-infrared spectroscopy, temperature influence.

Review of the Application of Near-Infrared Spectroscopy Technology to Determine the Chemical Composition of Animal Manure

Animal manure contains a variety of chemical constituents that are highly valuable to agriculture, including nitrogen, phosphorus, potassium, and metal micronutrients. Although appropriately applied manure has numerous positive attributes, the excessive application of manure may lead to pollution of the atmosphere, water, or soil. To reconcile precision agriculture and the potential negative environmental influences of animal manure, it is necessary to develop rapid and robust methods to evaluate the chemical composition of animal manure. This paper summarizes recent advances in near-infrared reflectance spectroscopy (NIRS) in predicting moisture, dry matter, organic matter, nitrogen, phosphorus, carbon, and metal content in animal manure. The results indicate the high potential of NIRS as an efficient tool for monitoring the chemical composition of animal manure. Future prospects and needs related to increasing the feasibility of the industrial application of NIRS and improving NIRS prediction precision in determining the chemical composition of animal manure are discussed.

Determination of Protein and Moisture in Fishmeal by Near-Infrared Reflectance Spectroscopy and Multivariate Regression Based on Partial Least Squares

The potential of Near Infrared Reflectance Spectroscopy (NIRS) as a fast method to predict the Crude Protein (CP) and Moisture (M) content in fishmeal by scanning spectra between 1000 and 2500 nm using multivariate regression technique based on Partial Least Squares (PLS) was evaluated. The coefficient of determination in calibration (R 2 C) and Standard Error of Calibration (SEC) were 0.95 and 14.03 g / kg Dry Matter (DM) and 0.80 and 3.52 g / kg, for CP and M content, respectively. This study proved that the application of NIRS using PLS is well fitted to evaluate the protein and moisture content of fishmeal.

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of 2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients of determination (R2) were found to be 0.89–0.99 depending on the mathematical algorithm used, the data pre-processing applied and the sample type used. The corresponding values for the root mean square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively, again depending on the chemometric treatment applied to the data and sample type. In addition, adopting a qualitative approach with the spectral data and applying PCA, it was possible to discriminate between the four samples types and also, by generation of Cooman’s plots, possible to distinguish between adulterated and non-adulterated samples.

Application of NIRS for predicting fatty acids in intramuscular fat of rabbit

The aim of this study was to evaluate the use of near infrared reflectance spectroscopy (NIRS) for predicting fatty acid content in intramuscular fat to be applied in rabbit selection programs. One hundred and forty three freeze-dried Longissimus muscles (LM) were scanned by NIRS (1100–2498nm). Modified Partial Least Squares models were obtained. Equations were selected according to standard error of cross validation (SECV) and coefficient of determination of cross validation (R2CV). Residual predictive deviation of cross validation (RPDCV) was also studied. Accurate predictions were reported for IMF (R2CV=0.98; RPDCV=7.57), saturated (R2CV=0.96; RPDCV=5.08) and monounsaturated FA content (R2CV=0.98; RPDCV=6.68). Lower accuracy was obtained for polyunsaturated FA content (R2CV=0.83; RPDCV=2.40). Several individual FA were accurately predicted such as C14:0, C15:0, C16:0, C16:1, C17:0, C18:0, C18:1 n-9, C18:2 n-6 and C18:3 n-3 (R2CV=0.91-0.97; RPDCV>3). Long chain polyunsaturated FA and C18:1 n-7 presented less accurate prediction equations (R2CV=0.12-0.82; RPDCV<3).

Application of Near-Infrared Reflectance Spectroscopy to the Evaluation of D- chiro-lnositol, Vitexin, and Isovitexin Contents in Mung Bean

Abstract Mung bean ( Vigna radiata L.) is rich in D- chiro-inositol (DCI), vitexin, and isovitexin, which has beneficial effects on antidiabetic and inhibits the formation of advanced glycation end-products. In this study, near-infrared reflectance spectroscopy (NIRS) was used to predict the contents of DCI, vitexin, and isovitexin in mung bean. The spectra data were linearized with those determined by high-performance liquid chromatography (HPLC). The models for predicting the DCI, vitexin, and isovitexin contents in mung bean were developed using partial least-squares (PLS) algorithm. Cross-validation procedures indicated good correlations between HPLC data and NIRS predictions ( R 2=0.90 for DCI, R 2=0.81 for vitexin, and R 2=0.90 for isovitexin). The predictive contents of DCI, vitexin, and isovitexin ranged from 2.082 to 3.084%, 1.277 to 1.307%, and 0.5998 to 0.6286%, respectively. The results showed that NIRS, a well-established and widely applied technique, could be applied to rapid detection of DCI, vitexin, and isovitexin contents in mung bean.

Foraging selectivity of three goat breeds in a Mediterranean shrubland

Foraging behaviors of the Damascus, Mamber and Boer goat breeds were compared on shrublands of the South Carmel mountain ridge of Israel. Dietary choice was determined for a group of yearling animals of each breed (n=11 or 12), housed and grazed separately to prevent social facilitation, during a total of 4 (Mamber) or 5 (Damascus and Boer) sessions of four days each in fall 2004 and spring 2005. The proportions in the diet (including concentrate feed) of three main dietary components –Pistacia lentiscus L. (20% tannins), Phillyrea latifolia L. (3% tannins) and herbaceous vegetation – were determined by application of near-infrared reflectance spectroscopy to faecal samples (n=147). On average, P. lentiscus constituted 14.9% of the DM ingested by Damascus goats, but only 5.6 and 5.0% of that ingested by Mamber and Boer goats, respectively. In the fall season, Damascus goats ingested diets richer in tannins than did Mamber or Boer goats (P<0.001). In the spring, the contribution of herbaceous vegetation to DM intake was higher in Mamber goats (38.5%) than in the other breeds (27.7%). Boer goats selected the most nutritious diets in terms of crude protein (CP) content and in vitro dry matter digestibility (IVDMD). Despite their differences in foraging selectivity, the local Damascus and Mamber goats selected similar diets in terms of CP and IVDMD. Our data suggests that, of the three breeds examined, the Damascus should provide the best control of P. lentiscus encroachment and the least competition with cattle for green grass in the spring. These findings may help to develop ecologically sound ways of controlling the spread of the tannin-rich shrub P. lentiscus L., which threatens rangeland biodiversity and amenity values in the Mediterranean climatic region of Israel.

Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits

Physical and color characteristics of chicken meat were investigated on 193 animals by directly applying a fiberoptic probe to the breast muscle and using the visible-near-infrared (NIR) spectral range from 350 to 1,800 nm. Data on pH was recorded 48 h postmortem (pH); lightness (L*), redness (a*), and yellowness (b*) 48 h postmortem; thawing and cooking losses and shear force after freezing. Partial least squares regressions were performed using untreated data, raw absorbance data (log(1/R)), and multiplicative scatter correction plus first or second derivative spectra. Models were validated using full cross-validation, and their predictive ability was determined by root mean square error of cross-validation (RMSE(CV)) and correlation coefficient of cross-validation (r(cv)). Means (±SD) of pH, L*, a*, b*, thawing loss, cooking loss, and shear force were 5.83 ± 0.13, 44.54 ± 2.42, -1.90 ± 0.62, 3.21 ± 3.28, 4.84 ± 2.44%, 19.39 ± 2.95%, and 16.08 ± 3.83 N, respectively. The best prediction models were developed using log(1/R) spectra for b* (r(cv) = 0.93; RMSE(CV) = 1.16) and a* (r(cv) = 0.88; RMSE(CV) = 0.29), while a medium predictive ability of NIR was obtained for pH, L*, and thawing and cooking losses (r(cv) from 0.69 to 0.76; RMSE(CV) from 0.01 to 1.73). Finally, predicted model for shear force (r(cv) = 0.41; RMSE(CV) = 3.18) was unsatisfactory. Results suggest that NIR is a feasible technique for the assessment of several quality traits of intact breast muscle.

Opportunities for, and limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review

Near infrared reflectance spectroscopy (NIRS) is a cost- and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r2), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.Key words: Near infrared reflectance spectroscopy, soil analysis, calibration

Application of near-infrared reflectance spectroscopy for the estimation of protein and L-canavanine contents in seeds of one-flowered vetch (Vicia articulata Hornem.)

In this work it has been evaluated the effectiveness of near-infrared reflectance spectroscopy (NIRS) for the estimation of two constituents affecting the nutritional quality of the seeds of one-flowered vetch (Vicia articulata Hornem.): protein and L-canavanine, a toxic non-protein amino acid. The NIRS calibrations showed good statistics with coefficients of multiple determination (R2) of 0.97 for total protein and 0.95 for L-canavanine. The developed equations were further used to estimate the contents of protein and L-canavanine of a set of unknown samples (external validation). The equation for total protein was able to predict with an accuracy similar to that of the reference method showing a correlation (r2) between the reference and predicted values of 0.95. In the case of L-canavanine, r2 was only 0.72 and the equation was only effective to discriminate samples into groups of low, medium and high contents.

Application of near-infrared reflectance spectroscopy to evaluate the lutein and β-carotene in Chinese kale

There are wide ranges of lutein and β-carotene in Chinese kale. This work assessed the capability of near-infrared reflectance spectroscopy (NIRS) to predict the contents of lutein and β-carotene in Chinese kale against HPLC analysis. Compared to other regression methods, the regression of modified partial least squares with math treatments of 2,4,4,1 (where the first number of 2 represents the second derivative of log 1/ R, the second of 4 is the gap in data points over which the derivative is calculated, the third and fourth numbers refer to the number of data points used in the first and second smoothing, respectively) showed the best results. The calibration equations of lutein and β-carotene were characterized by the coefficients of determination (RSQ) of 0.983 and 0.982 and standard error of 0.056 and 0.131 mg g −1 DW, respectively. In cross-validation, high 1 minus variance ratio (1 − VR) of 0.955 and 0.966 and standard error of 0.092 and 0.179 mg g −1 DW for lutein and β-carotene were obtained, respectively. In external validations, the RSQ were 0.926 and 0.875 with standard error of 0.131 and 0.367 mg g −1 DW, respectively. These results showed that NIRS could be used for the determination of carotenoids in Chinese kale.

Total and individual carotenoid profiles in Solanum phureja cultivated potatoes: II. Development and application of near-infrared reflectance spectroscopy (NIRS) calibrations for germplasm characterization

Calibration equations to estimate total and individual carotenoid concentrations by near-infrared reflectance spectroscopy (NIRS) in Solanum phureja potato germplasm were developed, validated and applied to the characterization of 152 S. phureja germplasm accessions. The cross, external and independent validation of the calibrations indicate that total carotenoids and zeaxanthin concentrations can be estimated by NIRS with high accuracy and that NIRS can be used to differentiate accessions with low, medium and high concentrations of violaxanthin, antheraxanthin, lutein or β-carotene. Significant variability was revealed for total and all individual carotenoids measured. Two varieties with significant zeaxanthin concentrations (above 1000 μg 100 g −1 FW) and a group of 43 accessions with relatively high β-carotene concentrations (above 10 μg 100 g −1 FW) were identified. The positive relation between total carotenoids and zeaxanthin as well as their negative relation with β-carotene reported for 23 accessions in the companion manuscript was upheld. The use of NIRS in estimating and characterizing a large germplasm collection has permitted the identification of accessions with high total carotenoids and zeaxanthin and relatively high β-carotene concentrations at low cost and in reduced time compared to HPLC. This can be applied to documentation of descriptive and potentially beneficial characteristics of potato genebank materials, the assessment of the food value of present varieties, and the selection of parents in a breeding program oriented to nutritional enhancement of potato.