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1.
The aim of the present study is to develop a methodology for the rapid estimation of taro (Colocasia esculenta) quality. Chemical analyses were conducted on 315 accessions for major constituents (starch, total sugars, cellulose, proteins, and minerals). NIRS calibration equations, developed on a calibration set composed of 243 accessions, showed high explained variances in cross-validation (r(2)(cv)) for starch (0.89), sugars (0.90), proteins (0.89), and minerals (0.90) but poor response for amylose (0.44) and cellulose (0.61). The predictions were tested on an independent set of 58 randomly selected accessions. The r(2)(pred) values for starch, sugars, proteins, and minerals were, respectively, of 0.76, 0.74, 0.85, and 0.85 with ratios of performance to deviation (RPD) of 3.41, 4.01, 3.78, and 3.64. New calibration equations developed on 303 accessions confirmed good RPD values for starch (3.30), sugars (4.13), proteins (3.61), and minerals (3.74). NIRS could be used to predict starch, sugars, proteins, and minerals contents in taro corms with reasonably high confidence.  相似文献   

2.
Near-infrared spectroscopy (NIRS) is a well-established technique for determining the components of foods. Sample preparation for NIRS is easy, making it suitable for breeding and/or quality evaluation, for which a large number of samples should be analyzed. We aimed to assess the feasibility of NIRS to estimate parameters that seem to influence consumers' perception of the seed coat of common beans: dietary fiber (DF), uronic acids (UA), ashes, calcium, and magnesium. We used reference methods to analyze ground seed coats of 90 common bean samples with a wide range of genetic variability and cultivated at many locations. We registered the NIR spectra on intact beans and ground seed coat samples. We derived partial least-squares (PLS) regression equations from a set of calibration samples and tested their predictive power in an external validation set. For intact beans, only RER values for ashes and calcium are good enough for very rough screening. For ground seed coat samples, the RPD and RER values for ashes (3.49 and 14.09, respectively) and calcium (3.57 and 12.70, respectively) are good enough for screening. RPD and RER values for DF (2.60 and 9.15, respectively) and RER values for magnesium (6.57) also enable rough screening. A poorer correlation was achieved for UA. We conclude that NIRS can help in common bean breeding research and quality evaluation.  相似文献   

3.
The purpose of the present study was to evaluate the capability of near infrared spectroscopy (NIRS) as a simple method to monitor the lipid content of garbage compost, which is a potential inhibitor of plant growth. We conducted a cultivation experiment with vegetable mock pak choy ( Brassica rapa L. Parachinensis Group) using two application rates of four garbage composts that differed in lipid content. The input of lipid from the compost to the field showed a significant negative correlation with germination rate and plant height in the initial growth stage. Reflectance spectra of untreated and freeze-dried and milled compost samples were taken using a scanning monochromator. Second-derivative spectra and multiple regression analysis were used to develop calibration equations for lipid and moisture contents. The calibration was carried out with the short wavelength region ([SWR] 800–1100 nm) and the long wavelength region ([LWR] 1100–2500 nm) separately. The calibration equations with the LWR were more accurate than those with the SWR for lipid and moisture determinations. The accuracies of the calibration equations for untreated samples were comparable to those for freeze-dried and milled samples. In conclusion, we suggest that the application rate of garbage compost can be determined by measuring the lipid content of untreated samples by NIRS.  相似文献   

4.
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.  相似文献   

5.
Abstract

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were “successful” according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.  相似文献   

6.
In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQCAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g?1), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g?1) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.  相似文献   

7.
Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were "successful" according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.  相似文献   

8.
基于相似光谱匹配预测土壤有机质和阳离子交换量   总被引：4，自引：1，他引：3  
土壤可见光-近红外波段光谱(350~2 500 nm)包含了大量的土壤属性信息,相同类型的土壤具有相似的光谱曲线特征,但相似光谱曲线是否具有相似的属性含量?探讨此问题可为土壤光谱库的应用提供依据,从而最终服务于快速获取土壤信息技术体系的构建。该研究以安徽宣城为研究区,根据母质、地形特征和土地利用等信息,采集91个典型土壤剖面,共含400个土壤发生层样品,测定了有机质(soil organic matter,SOM)和阳离子交换量(cation exchange capacity,CEC)含量,同时采用VARIAN公司的Cary 5000分光光度计测定了土壤光谱,并将光谱数据变换为反射率(R)、反射率一阶导数(FDR)和吸收度(Log(1/R))3种形式。该文采用光谱角(spectral angle mapper,SAM)、偏最小二乘回归(partial least square regression,PLSR)和SAM-PLSR(spectral angle mapper-partial least square regression,SAM-PLSR)3种方法预测土壤SOM和CEC。SAM方法是通过对测试集104个光谱曲线与参考集的296个光谱曲线进行相似性计算,并以此实现土壤SOM和CEC含量的预测。SAM-PLSR方法以SAM算法下的匹配结果作为建模样本建立PLSR模型和进行预测分析。结果表明,具有相似光谱曲线的土壤具有相似的SOM和CEC含量,SAM算法下相似光谱匹配可直接预测SOM(R2=0.78,RPD=2.17)和CEC(R2=0.82,RPD=2.41)。PLSR方法可很好地预测SOM(R2=0.87,RPD=2.77)和CEC(R2=0.87,RPD=2.59);相较之下,SAM-PLSR方法不仅可以更加准确预测SOM(R2=0.89,RPD=3.00)和CEC(R2=0.91,RPD=3.06),而且大大减少了建模样本的数量。该研究使可见光-近红外光谱可更加高效地用于土壤属性分析,并为土壤光谱数据库的建设及应用提供技术参考。  相似文献   

9.
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.  相似文献   

10.
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.  相似文献   

11.
A study was conducted to investigate methods of improving a near-infrared transmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining amylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations ranging in starch-amylose content (SAC) from -8.5 to 76%, relative to a standard curve. The influence of three factors were examined including comparing calibrations made against SAC versus grain amylose content (GAC), developing calibrations using partial least squares (PLS) analysis versus artificial neural networking (ANN), and using all samples in the calibrations set versus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PLS analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard error of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all NITS models. The study revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did not improve performance statistics except for PLS models for SAC in which a decrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples with SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be useful when a rough screening method is needed for SAC. For example, NITS may be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples.  相似文献   

12.
The objective of this study was to develop a near‐infrared (NIR) imaging system to determine rice moisture content. The NIR imaging system fitted with 15 band‐pass filters (wavelengths of 870–1,014 nm) was used to capture the spectral image. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both near‐infrared spectrometry (NIRS) and the NIR imaging system to determine the moisture content of rice. Comprehensive performance comparison among MLR, PLSR, and ANN approaches has been conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six significant wavelengths selected by the MLR model, which had high correlation with the moisture content of rice, were used as the input data of the ANN. The performance of the developed system was evaluated through experimental tests for rice moisture content. This study adopted the coefficient of determination (rval2), the standard error of prediction (SEP), and the relative performance determinant (RPD) as the performance indices of the NIR imaging system with respect to the tests of rice moisture content. Utilizing these three models, the analysis results of rval2, SEP, and RPD for the validation set were within 0.942–0.952, 0.435–0.479%, and 4.2–4.6, respectively. From experimental results, the performance of NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided a high prediction capacity for the determination of moisture in rice samples. These results indicated that the NIR imaging system developed in this study can be used as a device for the measurement of rice moisture content.  相似文献   

13.
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.  相似文献   

14.
A germplasm collection consisting of 1475 entries from 21 species of Brassica, including 36 lower taxa, was evaluated for the fatty acid composition of the seed oil. A total of 358 entries representing the taxonomic variability in the collection were selected and analysed by gas-liquid chromatography (GLC). The remaining 1117 entries were analysed by near-infrared reflectance spectroscopy (NIRS), after developing multi-species calibration equations. The results demonstrated that NIRS is an effective technique to assess variability for oleic, linoleic, linolenic and erucic acid in intact-seed samples of multiple Brassica species, provided that calibration equations be developed from sets containing large taxonomic and chemical variability. Some fatty acid ratios were used to estimate the efficiency of the different biosynthetic pathways. Two well-defined patterns were observed. The first one was characterised by high elongation efficiency and accumulation of high levels of erucic acid. The highest erucic acid content (>55% of the total fatty acids) was found in the cultivated species B. napus L., B. oleracea L., and B. rapa L., and in the wild species B. incana Tenore, B. rupestris Raf., and B. villosa Bivona-Bernardi, the three latter belonging to the B. oleracea group (n=9). The second pattern was characterised by high desaturation efficiency, resulting in the accumulation of high levels of the polyunsaturated linoleic and linolenic acid (up to more than 55%). The highest levels of these fatty acids were found in samples of B. elongata Ehrh., especially of the var. integrifolia Boiss. The utility of the reported variability for plant breeding is discussed.  相似文献   

15.
Breeding of high‐quality rice requires quick methods to evaluate the quality characteristics such as milling, grain appearance, nutritional, eating, and cooking qualities. Because routine measurements of these quality traits are time consuming and expensive, a rapid predictive method based on near‐infrared spectroscopy (NIRS) can be applied to measure these quality parameters. In this study, calibration models for measurement of grain quality were developed using a total of 570 brown and milled rice samples. The results indicated that the models developed from the spectra of brown rice for all the quality traits had the coefficient of determination for external validation (R2) larger than 0.64 except for gel consistency. The best model was developed for the protein content, with R2 of 0.94 for external validation. The model for the total score of physicochemical characteristics (TSPC), a comprehensive index reflecting all other traits, had R2 of 0.70 and SD/SEP of 1.70, which indicates that high or low TSPC for a given rice could be discriminated by NIRS. The models developed from brown rice were as accurate as those from milled rice. Results suggest that NIRS‐based predictions for rice quality traits may be used as indicator traits to improve rice quality in breeding programs.  相似文献   

16.
近红外光谱法测定玉米秸秆饲用品质   总被引：6，自引：1，他引：5  
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。  相似文献   

17.
To determine lignin content in triticale and wheat straws, calibration models were built using Fourier transform mid-infrared spectroscopy combined with partial least-squares regression. The best model for triticale and wheat straws was built using averaged spectra with raw spectrum in spectrum format and constant in path length as spectral pretreatments. The values of r(2), root-mean-square error of prediction (RMSEP), and residual predictive deviation (RPD) for the triticale straw model were 0.935, 0.305, and 3.89, respectively. The r(2), RMSEP, and RPD values for the wheat straw model were 0.985, 0.163, and 8.50, respectively. Both models showed good predictive ability. A model built using both triticale and wheat straws indicated that the values of r(2), RMSEP, and RPD were 0.952, 0.27, and 4.63, respectively. This model also showed good predictive ability and could predict lignin contents in triticale and wheat straws with the same high accuracy.  相似文献   

18.
A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (Rv), 1.119 g kg?1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with Rv = 0.952, RMSEP = 0.633 g kg?1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.  相似文献   

19.
可见/近红外光谱分析秸秆-煤混燃物的秸秆含量   总被引：1，自引：1，他引：0  
快速检测秸秆-煤混燃物对生物质混燃发电中补贴政策的制定具有重要意义。该研究采用可见/近红外光谱法定性判别秸秆、煤和秸秆-煤混燃物,定量分析秸秆-煤混燃物中秸秆含量。收集并制备秸秆样品80个（粒径小于80 mm）、煤样品9个（粒径小于10 mm）,制备秸秆质量分数为70%～99%的秸秆-煤混燃物样品120个（混燃物1）、秸秆分数含量为1%～30%的秸秆-煤混燃物样品120个（混燃物2）。使用FOSS NIRS DS 2500型光谱仪获取样品光谱。分别使用偏最小二乘判别法（PLS-DA）建立定性分析模型,使用改进的偏最小二乘法（MPLS）建立定量分析模型。结果显示,在秸秆和混燃物1之间进行判别,使用1100～2500 nm谱区,正确判别率为90.00%;在煤和混燃物2之间进行判别,使用400～2500 nm谱区,正确判别率为71.88%;定量分析混燃物1和混燃物2中秸秆含量,相对分析误差分别为2.32（400～2500 nm谱区）和1.48（400～1100 nm谱区）。研究结果表明,1100～2500 nm谱区较适合秸秆和混燃物1之间的判别,该谱区同样适合定量分析混燃物1中秸秆含量。400～1100 nm谱区较适合煤和混燃物2之间的判别,该谱区同样适合定量分析混燃物2中秸秆含量。可见/近红外光谱结合化学计量学是快速定性和定量分析大粒度秸秆-煤混燃物的可行方法。  相似文献   

20.
Near-infrared spectroscopy (NIRS) was used for the simultaneous prediction of exopolysaccharide (EPS; 0-3 g/L) and lactic acid (0-59 g/L) productions as well as lactose (0-68 g/L) concentration in supernatant samples from pH-controlled batch cultures of Lactobacillus rhamnosus RW-9595M in supplemented whey permeate medium. To develop calibration equations, the correlation between the second derivative of 164 NIRS transmittance spectra and concentration data obtained with reference methods was calculated at the wavelength between 1653-1770 and 2041-2353 nm, using a partial least-squares method (PLS). The lactic acid and lactose concentrations were measured by HPLC, and the EPS concentration was estimated by a new ultrafiltration method. The PLS correlation coefficient (R(2)) and the standard error of cross-validation for the calibrations were 91% and 0.26 g/L for EPS, 99% and 2.54 g/L for lactic acid, and 98% and 3.32 g/L for lactose, respectively. The calibration equations were validated with 45 randomly selected culture samples from 6 cultures that were not used for calibration. A high agreement between data of the reference methods and those of NIRS was observed, with correlation coefficients and standard errors of prediction of 99% and 1.64 g/L for lactic acid, 99% and 4.5 g/L for lactose, and 91% and 0.32 g/L for EPS. The results suggest that NIRS could be a useful method for rapid monitoring and control of EPS lactic fermentations.  相似文献   

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