二维相关光谱结合偏最小二乘法测定牛奶中的掺杂尿素

为了检验牛奶中是否掺杂尿素并将其量化测定,配置含有尿素质量浓度范围为1～20g/L之间40个牛奶样品,以掺杂物尿素浓度为外扰,分别研究了掺杂尿素牛奶的二维相关(近红外-近红外,中红外-中红外,近红外-中红外)光谱特性,在此基础上,分别选择随浓度变化大的4200～4800cm-1和1400～1704cm-1为建模区间,采用偏最小二乘方法建立定量分析模型。研究结果表明:4200～4800cm-1建模分析效果优于1400～1704cm-1建模结果,其交叉验证均方根误差为0.266g/L,对未知样品集预测相关系数达到0.999,预测均方根误差为0.219g/L,这表明所建模型具有较好的预测效果。该方法无需样品处理,成本低,为快速判别牛奶是否掺杂提供了一种新的可能的方法。     

Investigation of the potential utility of single-bounce attenuated total reflectance Fourier transform infrared spectroscopy in the analysis of distilled liquors and wines

A new Fourier transform infrared (FTIR) spectroscopic method based on single-bounce attenuated total reflectance (SB-ATR) spectroscopy was developed for the analysis of distilled liquors and wines. For distilled liquors, a partial least-squares (PLS) calibration was developed for alcohol determination based on the SB-ATR/FTIR spectra of mixtures of ethanol and distilled water. An independent set of 12 different distilled liquor samples was predicted from the PLS calibration, and a standard deviation of the differences for accuracy (SDD(a)) between actual and predicted values of 0.142% (v/v) was obtained. The potential utility of SB-ATR/FTIR spectroscopy for the analysis of wines was initially evaluated based on a comparison with Fourier transform near-infrared (FT-NIR) spectroscopy and FTIR spectroscopy using a flow-through transmission cell. PLS calibrations for alcohol, total reducing sugars, total acidity and pH were developed using pre-analyzed wine samples (n = 28), and for SB-ATR/FTIR spectroscopy, the SDD(a) for the leave-one-out cross-validation statistics were of the order of 0.100% (v/v), 0.707 g L(-1), 0.189 g L(-1) (H2SO4), and 0.230, respectively. Overall, the SB-ATR/FTIR results were better than those obtained using FT-NIR spectroscopy and comparable to those obtained with transmission FTIR spectroscopy. A PLS calibration based on preanalyzed wine samples (n = 72) for the prediction of 11 different components and parameters in wines by SB-ATR/FTIR spectroscopy was subsequently developed and validated using an independent sample set (n = 77). Good coefficients of correlation between the reference and predicted values for the validation set were obtained for most of the components and parameters except citric acid, volatile acids, and total SO2. The results of this study demonstrate the suitability of SB-ATR/FTIR spectroscopy for the routine analysis of distilled liquors and wines.     

Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy

Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination ( r (2)) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r (2) of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods ( r (2) < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.     

Use of NIRS for quantification of mangiferin and hesperidin contents of dried green honeybush (Cyclopia genistoides) plant material

Cyclopia genistoides, normally used for the preparation of an herbal tea, honeybush, is a good source of the bio-active compounds mangiferin and hesperidin and is in demand for the preparation of xanthone-enriched extracts. Near-infrared spectroscopy (NIRS) was used to develop calibration models to predict the mangiferin and hesperidin contents of the dried green plant material. NIRS measurements of plant material and pure compounds were performed in diffuse reflectance mode. The calibration sets for mangiferin and hesperidin contents ranged from 0.7 to 7.21 and 0.64-4.80 g/100 g, respectively. Using independent validation, it was shown that the NIRS calibration models for the prediction of mangiferin (SEP=0.46 g/100 g; R2=0.74; and RPD=1.96) and hesperidin (SEP=0.38 g/100 g; R2=0.72; and RDP=1.90) contents of the dried plant material are adequate for screening purposes, based on RPD values.     

Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal

Near-infrared reflectance spectroscopy (NIRS) calibrations were developed to enable the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important protein-rich feed ingredients. More than 1000 samples of global origin collected over four years were analyzed on amino acids following the official methods of the United States and the European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with independent samples for soy and meat meal products and compared to the amino acid predictions using linear crude protein regressions. With a few exceptions, validation showed that 85-98% of the amino acid variance in the samples could be explained using NIRS. NIRS predictions compared to reference results agree excellently, with relative mean deviations below 5%. Especially for meat and poultry meals, NIRS can predict amino acids much better than crude protein regressions. By enabling the amino acid analysis of many samples to be completed in a short time, NIRS can improve the accuracy of feed formulation and thus the quality and production costs of mixed feeds.     

近红外光谱法快速测定土壤碱解氮、速效磷和速效钾含量

运用偏最小二乘法(PLS)和人工神经网络(ANN)方法分别建立了0.9 mm筛分风干黑土土壤碱解氮、速效磷和速效钾含量预测的近红外光谱(NIRS)分析模型。使用偏最小二乘算法建立的碱解氮、速效磷和速效钾校正模型的决定系数R²分别为0.9520、0.8714和0.7300，平均相对误差分别为3.42%、13.40%和7.40%。人工神经网络方法建立的碱解氮、速效磷和速效钾校正模型的决定系数分别为0.9563、0.9493和0.9522，相对误差分别为2.67%、6.48%和2.27%，测试集仿真的相对误差分别为5.44%、16.65%和7.87%。结果表明，人工神经网络方法所建立的校正模型均优于偏最小二乘法所建模型；用近红外光谱分析法预测土壤碱解氮含量是可行的，而速效磷、速效钾模型的测试集样品仿真的相对误差较大，其预测可行性还需做进一步研究。     

大米胶稠度近红外光谱分析数学模型的建立

胶稠度是评价大米蒸煮食用品质的重要指标之一。研究了运用近红外光谱分析技术检测大米胶稠度的测试原理，对60个样品的光谱数据用偏最小二乘法(PLS)建立了测定大米胶稠度的数学模型，其回判结果与化学分析值之间的相关系数为0.95，建模标准差为0.66；用41个样品对建立的数学模型进行了交叉验证，其检测结果与用标准化学分析方法测得结果的相关系数达0.92，预测标准差为0.78。试验证明，可以利用近红外光谱分析技术对大米胶稠度进行快速检测。     

Estimating hydrogen cyanide in forage sorghum ( Sorghum bicolor ) by near-infrared spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (NIRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R(2)) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R(2) = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R(2) = 0.847 and standard error of calibration (SEC) = 0.050% and a R(2) = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C═O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.     

油用牡丹单粒种子含油量NIRS模型的建立

为了建立油用牡丹单粒种子含油量的近红外测定模型,便于高含油量单株的选育,采用索氏抽提法测试了200份油用牡丹凤丹单粒种子的含油量,并应用近红外反射光谱技术(NIRS)采集了200份样品的光谱数据,通过偏最小二乘法(PLS)和主成分回归法(PCR)构建了油用牡丹单粒种子含油量的数学模型。结果表明,索氏抽提法中,均匀粉碎后的油用牡丹籽样品干燥烘焙条件为105℃ 2 h,牡丹籽抽提时间为20 h,测出的含油量变化范围在10%~28%之间,籽油含量基本符合正态分布。NIRS法构建的模型最佳参数为:采用PLS法,光程固定,一阶导数消除背景,数据平滑处理采用Norris derivative filter的方法,平滑参数选用5和3。内部交叉检验校正相关系数r₁为0.980 1、预测相关系数r₂为0.957 6、校正均方根误差(RMSEC)为0.463、预测均方根误差(RMSEP)为0.705。外部检验相关系数达 0.957 6, 平均误差小于3%。本试验所构建的牡丹单粒种子含油量的NIRS模型可靠,可以用于分析油用牡丹单粒种子的含油量。     

Glucan and fructan production by sourdough Weissella cibaria and Lactobacillus plantarum

After a large screening on sourdough lactic acid bacteria, exopolysaccharide (EPS)-forming strains of Weissella cibaria, Lactobacillus plantarum, and Pediococcus pentosaceus were selected. After 6 days of incubation at 30 degrees C, the synthesis of EPS in MRS-based broth ranged from 5.54 to 7.88 mg mL-1. EPS had an apparent molecular mass of ca. 104 Da. As shown by carbohydrate consumption, the synthesis of EPS was found from sucrose only. Two types of homopolysaccharides were synthesized: glucans simultaneously with growth and fructans after 1 day of incubation. Two protein bands of ca. 180-200 kDa were in situ detected on SDS-PAGE gels incubated with sucrose. PCR products of ca. 220 bp were found for L. plantarum PL9 (100% of identity to putative priming glycosyltransferase of L. plantarum WCFS1) and W. cibaria WC4 (80% of identity to putative glycosyltransferase, epsD, of Bacillus cereus G9241) by using hybrid primers for the priming gtf genes. Degenerated primers DexreuR and DexreuV showed a unique PCR product, and the predicted amino acid sequences were identical for W. cibaria WC4 and L. plantarum PL9. The sequence had similarity with polysaccharide biosynthesis glycosyltransferases. W. cibaria WC4 or L. plantarum LP9 synthesized ca. 2.5 g kg-1 EPS during sourdough fermentation with sucrose added. Compared to the sourdough started with an EPS-negative strain, the sourdough started with W. cibaria WC4 or L. plantarum LP9 increased the viscosity, and the resulting bread had higher specific volume and lower firmness. The synthesis of EPS by selected sourdough lactic acid bacteria could be considered as a useful tool to replace the additives for improving the textural properties of baked goods.     

Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat,barley, corn,triticale, wheat bran/middlings,rice bran,and sorghum

Further NIRS calibrations were developed for the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important cereals and brans or middlings for animal feed production. More than 1100 samples of global origin collected over five years were analyzed for amino acids following the Official Methods of the United States and European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with 98 independent samples for wheat and 78 samples for corn and compared to amino acid predictions using linear crude protein regression equations. With a few exceptions, validation showed that 70-98% of the amino acid variance in the samples could be explained using NIRS. Especially for lysine and methionine, the most limiting amino acids for farm animals, NIRS can predict contents in cereals much better than crude protein regressions. Through low cost and high speed of analysis NIRS enables the amino acid analysis of many samples in order to improve the accuracy of feed formulation and obtain better quality and lower production costs.     

Quantification of fatty acids in forages by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was evaluated as a possible alternative to gas chromatography (GC) for the quantitative analysis of fatty acids in forages. Herbage samples from 11 greenhouse-grown forage species (grasses, legumes, and forbs) were collected at three stages of growth. Samples were freeze-dried, ground, and analyzed by GC and NIRS techniques. Half of the 195 samples were used to develop an NIRS calibration file for each of eight fatty acids, with the remaining half used as a validation data set. Spectral data, collected over a wavelength range of 1100-2498 nm, were regressed against GC data to develop calibration equations for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. Calibration equations had high coefficients of determination for calibration (0.93-0.99) and cross-validation (0.89-0.98), and standard errors of calibration and cross-validation were < 20% of the respective means. Simple linear regressions of NIRS results against GC data for the validation data set had r2 values ranging from 0.86 to 0.97. Regression slopes for C12:0, C14:0, C16:0, C18:0, C16:1, C18:2, and C18:3 were not significantly different (P = 0.05) from 1.0. The regression slope for C18:1 was 1.1. The ratio of standard error of prediction to standard deviation was > 3.0 for all fatty acids except C12:0 (2.6) and C14:0 (2.9). Validation statistics indicate that NIRS has high prediction ability for fatty acids in forages. Calibration equations developed using data for all plant materials accurately predicted concentrations of C16:0, C18:2, and C18:3 in individual plant species. Accuracy of prediction was less, but acceptable, for fatty acids (C12:0, C14:0, C18:0, C16:1, and C18:1) that were less prevalent.     

基于近红外光谱的灌浆期玉米籽粒水分小样本定量分析

玉米灌浆期含水率测定是考种育种的重要指标。为了节约样本且快速准确测定灌浆期玉米水分,该文应用近红外光谱技术,提出了基于小样本条件下的自举算法(Bootstrap)与基于x-y距离结合的样本划分方法(SPXY,sample set partitioning based on joint x-y distances)相结合的样本优化方法的偏最小二乘(PLS,partial least square)水分定量分析模型Bootstrap-SPXY-PLS模型。试验结果表明,当Bootstrap重抽样本次数等于500,样本数量大于等于10时,模型的性能稳定,并且随着样本数量增加,重抽样本次数相对减少;样本数量为10和50时,全谱Bootstrap-SPXY-PLS模型的预测均方根误差(RMSEP,root-mean-square error of prediction)均值分别为0.38%和0.40%,预测相关系数(correlation coefficients of prediction)分别为0.975 1和0.968 5,决定系数R~2分别为0.999 9和0.993 6;基于竞争性自适应重加权采样算法(CARS,competitive adaptive reweighed sampling)波长变量筛选后的CARS-Bootstrap-SPXY-PLS模型的预测均方根误差RMSEP均值分别为0.36%和0.35%,预测相关系数分别为0.973 6和0.975 0,模型决定系数R~2分别为0.924 5和0.918 0。因此,全谱Bootstrap-SPXY-PLS模型和CARS-Bootstrap-SPXY-PLS模型均具有稳定的预测能力,为玉米育种时灌浆期种子水分测定提供了一种稳定、高效的方法。     

Rapid analysis of methanol in grape-derived distillation products using near-infrared transmission spectroscopy

Samples of distillates derived from the production of wine-fortifying spirit were analyzed for methanol by gas chromatography (GC) and near-infrared spectroscopy (NIRS). NIRS calibration models were developed which could accurately predict methanol concentrations in samples of fortifying spirit that had been produced over a period of three years from four different commercial distillation facilities. The best accuracy of the predictive models, as measured by the standard error of prediction value, was 0.06 g/L methanol. Other distillation fractions, produced during preparation of commercial fortifying spirit, were also examined. The most useful NIRS calibration models used partial least squares regression on continuous spectra from a scanning instrument, but it was demonstrated that calibrations could also be developed with a smaller number of fixed wavelengths, using multiple linear regression models. NIRS offers the advantages of rapid analysis, with simple routine operation, and may offer the potential for in-line process control in the operation of a commercial distillation facility.     

Quantification of kavalactones and determination of kava (Piper methysticum) chemotypes using near-infrared reflectance spectroscopy for quality control in vanuatu

Kava ( Piper methysticum Forst f., Piperaceae) has anxiolytic properties and the ability to promote a state of relaxation without the loss of mental alertness. The rapid growth of the nutraceutical market between 1998 and 2000 has been stopped by a ban in Europe and Australia because of some suspicion of liver toxicity. It is now important to develop a fast, cheap, and reliable quality test to control kava exports. The aim of this study is to develop a calibration of the near-infrared reflectance spectroscopy (NIRS) using partial least-squares (PLS) regression. Two hundred thirty-six samples of kava roots, stumps, and basal stems were collected from the Vanuatu Agricultural Research and Technical Centre germplasm collection and from four villages. These samples, representing 45 different varieties, were analyzed using NIRS to record their absorption spectra between 400 and 2500 nm. A set of 101 selected samples was analyzed for their kavalactone content using HPLC. The results were used for PLS calibration of the NIRS. The NIRS prediction of the kavalactone content and the dry matter were in agreement with the HPLC results. There were good correlations between these two series of results, and coefficients ( R (2)) were all close to 1. The measurements were reproducible and had repeatability on par with the HPLC method. The NIRS system has been calibrated for the six major kavalactone content measurements, and it is suggested that this method could be used for quality control in Vanuatu.     

近红外光谱法测定玉米秸秆饲用品质

为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价,该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料,应用近红外光谱（NIRS）技术和偏最小二乘法（PLS）,采用一阶导数+中心化+多元散射校正的光谱数据预处理方法,构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802,交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342,外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191,各项标准差（SEC、SECV和SEP）为0.935～1.904,相对分析误差（RPD）均大于3。结果表明,各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。     

乳酸菌胞外多糖对逆境胁迫下水稻种子萌发及幼苗生长的影响

为探究乳酸菌胞外多糖浸种对酸、盐胁迫条件下水稻生长发育的影响,采用室内恒温培养试验,研究不同浓度乳酸菌胞外多糖对水稻种子萌发及相关生理代谢指标的影响。结果表明,采用不同浓度胞外多糖浸种可以显著提高逆境胁迫下水稻种子的发芽势、发芽率及发芽指数,促进水稻根系生长,随着胞外多糖浓度的升高,对水稻种子萌发的促进作用呈先升高后降低趋势;胞外多糖浓度为200 mg·L^-1时,缓解逆境胁迫伤害效果最为显著;pH值为3.0的酸胁迫下,与不添加胞外多糖相比,水稻种子的发芽势、发芽率及发芽指数分别提高了178.2%、38.7%和114.4%;7 mg·mL^-1NaCl胁迫下,发芽势、发芽率、发芽指数分别提高了152.9%、73.8%、103.0%,且差异均达显著水平(P<0.05);胞外多糖浸种可显著降低各胁迫下水稻幼芽丙二醛(MDA)含量,不同程度地提高过氧化氢酶(CAT)、过氧化物酶(POD)、超氧化物歧化酶(SOD)等抗氧化酶活性,同时胞外多糖对经过逆境胁迫的水稻种子播种后生长的幼苗也具有一定的促进作用,可缓解逆境胁迫对水稻造成的毒害。本研究为微生物源物增强作物抗逆性研究提供了理论依据。     

Use of visible and near infrared spectroscopy and least squares-support vector machine to determine soluble solids content and pH of cola beverage

Visible and near infrared (VIS/NIR) transmission spectroscopy and chemometric methods were utilized for the fast determination of soluble solids content (SSC) and pH of cola beverage. A total of 180 samples were used for the calibration set, whereas 60 samples were used for the validation set. Some preprocessing methods were applied before developing the calibration models. Several PLS factors, extracted by partial least squares (PLS) analysis, were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. The correlation coefficient (r), root mean square error of prediction (rmsEP), bias, and RPD were 0.959, 1.136, -0.185, and 3.5 for SSC, whereas 0.973, 0.053, 0.017, and 4.1 for pH, respectively. An excellent prediction precision was achieved by LS-SVM compared with PLS. The results indicated that VIS/NIR spectroscopy combined with LS-SVM could be applied as a rapid and alternative way for the fast determination of SSC and pH of cola beverage.     

Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm

Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.     

Quantitative Analysis of Chlorophyll and Protein in Alfalfa Leaves Using Fiber‐Optic Near‐Infrared Spectroscopy

The performance of near‐infrared (NIR) spectroscopy as a rapid technique for the estimation of chlorophyll and protein contents in alfalfa (Medicago sativa L.) was investigated. A fiber‐optic probe was employed directly on a total of 198 fresh leaves to measure spectra between 1100 and 2200 nm. Partial least squares (PLS) regression models were developed with a calibration set of 120 samples spanning a concentration range of 5.20–158.5 for the chlorophyll content index (CCI), 0.39–4.60 mg g^?1 (fresh weight) for the chlorophyll extracted with dimethylsulfoxide (DMSO), and 9.92–45.32% (dry matter) for protein content. The models obtained were validated with 78 independent samples. Standard errors of prediction of 12.49 were obtained for the CCI, 0.24 mg g^?1 for DMSO‐extracted chlorophyll, and 3.27% for the protein content. These results support the use of NIRS equipped with a fiber‐optic probe to monitor and assess the composition and quality of forages in a nondestructive way.