[Study on the Analysis of the Proportion of Mixed Samples with Near Infrared Spectroscopy and Non-Negative Coefficients Regression].

In the process of practical production, it is important to accurately analyze the proportion of mixed samples with high speed, which plays a great role for quality control and formulation design in food and agricultural processing. Traditional solution is to build statistical model with a large number of representative samples, which is both labor-intensive and time-consuming. In this paper, the proportion of alcohol and acids mixed samples, and their dilute solution mixed samples(used carbon tetrachloride (CCl4) which has no near-infrared absorption characteristics as the solvent medium)，as well as sheet tobacco leaf mixed samples are respectively analyzed by using near infrared spectroscopy, SG smooth and non-negative coefficients regression, which verifies the feasibility of analyzing the proportion of the mixed samples. The results show that, the analytic proportion of transmission spectra of alcohol and acids according to non-negative coefficients regression is closer to actual molar proportion with result error less than 4%. The result of the dilute solution is much better with error less than 4%. The analytic proportion of diffuse reflectance spectra of sheet tobacco leaf according to non-negative coefficients regression is highly accurate with error less than 10%. In the meantime, it has a highly consistency between actual spectra and analytic spectra of mixed samples; and the result of F-test and T-test shows that there is no significant difference between them and the confidence level is 0.01. It has the reliability of analytical proportion in theory. With the spectral data of several pure samples, the proportion of mixed samples can be thus analyzed, which has a good application prospect for quality control and formulation design in food and agricultural processing.

[Structure and Anti-Tumor Activity of Bovine α-Lactalbumin after Binding Linoleic Acid].

The structure changes of α-lactalbumin after binding oleic acid and linoleic acid, including hydrophobic amino acids, hydrophobic regions, tertiary structure, secondary structure, was studied by intrinsic fluorescence, ANS-binding intrinsic fluorescence and circular dichroism spectrum, respectively. The anti-tumor activity of the lactalbumin-oleic acid complex and lactalbumin-linoleic acid complex was measured using the methylene blue method. It can be seen from the fluorescence spectra that a significant red-shift from 331.07 to 337.67 nm and 337.60 nm of α-lactalbumin occurred after binding of oleic acid and linoleic acid, respectively. Together with the ANS-binding spectra, which exhibited a blue-shift (from 516.20 to 514.10 nm and 508.50 nm, respectively) with an increased fluorescence intensity, it can be indicated that binding of oleic acid and linoleic acid lead to the exposure of hydrophobic amino acids and hydrophobic regions. Results of circular dichroism spectra indicated the partial loss of the tertiary structure, and an decrease of ß-turn and random coil, which turn to the ß-sheet structure. Furthermore, the anti- tumor activity of the two complexes was verified on the three types of tumor cells. This study laid a theoretical basis for the development of anti-tumor drugs.

[Traceability of Wine Varieties Using Near Infrared Spectroscopy Combined with Cyclic Voltammetry].

To achieve the traceability of wine varieties, a method was proposed to fuse Near-infrared (NIR) spectra and cyclic voltammograms (CV) which contain different information using D-S evidence theory. NIR spectra and CV curves of three different varieties of wines (cabernet sauvignon, merlot, cabernet gernischt) which come from seven different geographical origins were collected separately. The discriminant models were built using PLS-DA method. Based on this, D-S evidence theory was then applied to achieve the integration of the two kinds of discrimination results. After integrated by D-S evidence theory, the accuracy rate of cross-validation is 95.69% and validation set is 94.12% for wine variety identification. When only considering the wine that come from Yantai, the accuracy rate of cross-validation is 99.46% and validation set is 100%. All the traceability models after fusion achieved better results on classification than individual method. These results suggest that the proposed method combining electrochemical information with spectral information using the D-S evidence combination formula is benefit to the improvement of model discrimination effect, and is a promising tool for discriminating different kinds of wines.

[Detection of Adulteration in Milk Powder with Starch Near Infrared].

Three China trademarks of milk powder called Mengniu, Yili, Wandashan were taken as testing samples. Each of them mixed varied amount of starch in different gradient, which were consisted of 32 adulterated milk powder samples mixed with starch, was taken as standard samples for constructing predicted model. To those 32 samples, the reflecting spectrum characteristics in middle wave of near infrared spectrum with Near Infrared Spectrum Analyzer (Micro NIR 1700) produced by JDSU Ltd. USA were collected for five repeats in five different days. The time span was nearly two months. Firstly, we build the model used the reflecting spectrum characteristics of those samples with biomimetic pattern recognition (BPR) arithmetic to do the qualitative analysis. The analysis included the reliability of testing result and stability of the model. When we took ninety percent as the evaluation threshold of testing result of CAR (Correct Acceptance Rate) and CRR (Correct Rejection Rate), the lowest starch content of adulterate milk powder in all tested samples which the tested result were bigger than that abovementioned threshold was designated CAR threshold (CAR-T) and CRR threshold (CRR-T). CAR means the correct rate of accepting a sample which is belong to itself, CRR means correct rate of refusing to accept a sample which is not belong to itself. The results were shown that, when we constructed a model based on the near infrared spectrum data from each of three China trademark milk powders, respectively, if we constructed a model with infrared spectrum data tested in a same day, both the CAR-T and CRR-T of adulterate starch content of a sample can reach 0.1% in predicting the remainder infrared spectrum data tested within a same day. The three China trademarks of milk powder had the same result. In addition, when we ignored the trademarks, put the spectrum data of adulterate milk powder samples mixed with the same content of starch of three China trademarks milk powder together to construct a model, the CAR-T of mixed starch content of a sample may reach 0.1%, the CRR-T can reach 1%, if the model construction and predicting were performed with near infrared spectrum data tested in a same day. However, the CAR-T can just stably reach up to 5% and the CRR-T have the same result, if the model construction and predicting were crossly performed with mixed near infrared spectrum data tested in different days. Furthermore, the correct recognizing threshold mixed starch of a sample can stably reach up to 1% and the CAR-T can reach 5%, if the model construction was based on near infrared spectrum data combined the previous four days to predict the output of the another day. On the other hand, we also engaged quantitative analysis to the starch content in milk power with two kinds of arithmetic (PLSR, LS-SVR). In contrast with the testing outputs, the reliability of both the CAR-T and CRR-T in qualitative analysis was further validated.

[Hyperspectral optimum wavelengths and Fisher discrimination analysis to distinguish different concentrations of aflatoxin on corn kernel surface].

Aflatoxin is a toxic metabolite widely existing in corn. In the present paper, the feasibility of detecting aflatoxin on corn kernel surface by hyperspectral imaging technology was verified. The corn called pioneer with the same shape is provided by Toxicology and Mycotoxin Research Unit. With methanol configuration, four different concentrations of aflatoxin solutions were prepared and dripped on every 30 corn kernels. Also other clean 30 kernels without aflatoxin dripped were prepared to be the control samples. Among the 150 kernel samples, 103 training samples and 47 validation samples were prepared randomly. Firstly, hyperspectral image in the range of 400 to 1 000 nm was collected. For eliminating the deviations in original spectrum, standard normal variate transformation (SNV) was adopted as pretreatment method. And then several optimum wavelengths were selected by the principle of minimum misdiagnosis rate. After that the selected optimum wavelengths were taken as the input of the Fisher discrimination analysis to discriminate the different concentrations of aflatoxin on the corn. Finally, the discrimination model based on four optimum wavelengths (812.42, 873.00, 900.36 and 965.00 nm) was built and the accuracy of the model was tested. Results indicate that the classification accuracy of calibration and validation set was 87.4% and 80.9% respectively. This method provides basis for designing the corresponding portable instrument and distinguishing aflatoxin produced by naturally metabolism in corn.

[Quality anlysis of the before redrying raw tobacco & after redrying sheet tobacco by using online near infrared spectroscopy].

In this paper, the 7 different origin before redrying raw tobacco & after redrying sheet tobacco's online near infrared spectroscopy were collected from sorting & redrying production line specifically for "ZHONGHUA" brand. By using the projection model bulit by different origin tobacco's online spectroscopy and the method of variance and correlation analysis, we studied the uniformity and similarity quality characteristics change before and after the redrying of tobacco, which can provide support for understanding the quality of the tobacco material and cigarette product formulations. This study show that selecting about 10,000 by equally spaced sampling time from a huge number of online near infrared spectroscopy, for modeling are feasible, and representative. After manual sorting, threshing, and redrying, the uiformity of each origin tobacco near-infrared spectroscopy can be increased by 10%~35%, homogeneity of the tobacco leaf has been significantly improved. After redrying, the similar relationship embodied in the origin also have significant changes, overall it reduce significantly, that shows the quality differences embodied by origin significantly improve, which can provide greater space for formulations, it shows the need for high-quality Chinese cigarette production requires large amounts of financial and human resources to implement cured tobacco processing. The traditional means of chemical analysis, it takes a lot of time and effort, it is difficult to control the entire processing chain, Near Infrared Spectroscopy with its rapid, non-destructive advantage, not only can achieve real-time detection and quality control, but also can take full advantage of near-infrared spectroscopy information created in the production process, which is a very promising online analytical detection technology in many industries especially in the agricultural and food processing industries.

[Analysis of tobacco site features using near-infrared spectroscopy and projection model].

In this paper, total of 5170 flue-cured tobacco samples collected from 2003 to 2012 in the domestic and foreign origin by Shanghai Tobacco Group Technical Center were tested by near infrared spectroscopy, including the typical upper leaves 1394, central 2550, the lower part of 1226. Using projection model of based on principal component and Fisher criterion (PPF), follow the projected results to get no statistically significant differences at adjacent principal components, and the number of principal components as little as possible, in this paper, four main components to build projection analysis model, the model results show that: the near-infrared spectral characteristics of the upper and lower leaves have a significant difference that can be achieved almost entirely distinguished; while the middle leaves with upper and lower have a certain degree of overlap, which is consistent to the actual situation of the continuity of tobacco leaf. At the same time, Euclidean distance between the predicted sample projection values and the mean projection values of each class in the model, a description is given for the prediction samples to quantify the extent of the site features, and its first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typically upper, upper to central, central to upper, typical central, central to the lower, the lower to central, typically the lower, or super-model range. The model was validated by 34 tobacco samples obtained from the re-drying process in 2012 with different origins and parts. This kind of analysis methods, not only can achieve discriminant analysis, and get richer feature attribute information, can provide guidance on the raw tobacco processing and formulations.

[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].

Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.

[Analysis of tobacco style features using near-infrared spectroscopy and projection model].

In the present paper, a total of 4,733 flue-cured tobacco samples collected from 2003 to 2012 in 17 provincial origins and 5 ecological areas were tested by near infrared spectroscopy, including the NONG(Luzhou) flavor 1,580 cartons, QING (Fen) flavor 2004 cartons and Intermediate flavor 1 149 cartons. Using projection model based on principal component and Fisher criterion (PPF), Projection analysis models of tobacco ecological regions and style characteristics were established. Reasonableness of style flavor division is illustrated by the model results of tobacco ecological areas. With the Euclidean distance between the predicted sample projection values and the mean projection values of each class in style characteristics model, a description is given for the prediction samples to quantify the extent of the style features, and their first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typical NONG, NONG to Intermediate, Intermediate to NONG, typical Intermediate, Intermediate to QING, QING to Intermediate, typical QING, QING to NONG, NONG to QING, or super-model range. The model was validated by 35 tobacco samples obtained from the re-dryingprocess in 2012 with different origins and parts. This kind of analysis methods not only can achieve discriminant analysis, but also can get richer feature attribute information and provide guidance to raw tobacco processing and formulations.

Spectroscopic evaluation of effects of heat treatments on the structures and emulsifying properties of caseins.

The effects of heat treatment (heating temperature and pH) on the structures and emulsifying properties of caseins were systematically studied by spectroscopy. Heat treatment from 60 to 100 degrees C resulted in an increase in their fluorescence intensity, hydrodynamic diameter, turbidity and emulsifying activity index, but decreased the size polydispersity of caseins. In the pH range of 5.5 to 7.0, the fluorescence intensity, hydrodynamic diameter, turbidity and emulsifying properties decreased with increased heating pH, but the size polydispersity of caseins increased with increased pH. The relationship between the surface fluorescence intensity and emulsifying activity was also investigated, revealing a correlation coefficient of 0.90. These results suggested that heat treatment could be used to modify the structures and emulsifying properties of caseins by appropriately selecting heating conditions.

[Discrimination of different aging methods of grape wine based on ATR infrared spectroscopy].

A total of 96 red wines aged with 3 kinds of methods were included in this study, including 44 wines aged in oak barrel, 26 wines aged in stainless steel tank added with oak chips and 26 wines aged in stainless steel tanks. The infrared spectra of the wines were scanned by Fourier transform infrared spectrometer with attenuated total reflection (ATR) accessories. To classify the 96 different aged wines, discriminant partial least squares (DPLS) method and support vector machine (SVM) method were used to establish models respectively. In order to examine the stability of the discriminant model, modeling was repeated 10 times with two-thirds of samples randomly selected as cross-validation. All the models had high discriminating power with the classification accuracy of the cross-validation and the validation all higher than 90%. These results suggest that the infrared ATR spectroscopy combined with pattern recognition method is a promising tool for discriminating different aging wines.

[Spectral properties of interaction between caffeic acid and milk protein and the change in antioxidant capacity].

The interaction between caffeic acid and milk protein (alpha-casein, beta-casein, kappa-casein, alpha-lactalbumin, beta-lactoglobulin) were studied in this work. The binding constant K(A), binding force, binding distance r(0) and transfer efficiency E were evaluated by UV-absorption and fluorescence spectroscopy. The antioxidant capacity of the combination was determined using both 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and ferric reducing antioxidant power (FRAP) assay. The results showed that the interaction between milk protein and caffeic acid resulted in the endogenous fluorescence quenching of milk protein, which belongs to a static quenching mechanism. The negative sign of free energy meant that the interaction process was spontaneous. The strength between caffeic acid and alpha-casein was electrostatic attraction (deltaH < 0, deltaS > 0), and that between beta-casein and alpha-Lactalbumin was hydrogen bonding (deltaH < 0, deltaS < 0). In addition, the strength of caffeic acid interacting with kappa-casein and beta-lactoglobulin was hydrophobic interaction (deltaH > 0, deltaS > 0). The binding distance (r(0) < 7 nm) proved that caffeic acid lead to a static quenching mechanism of milk protein. The antioxidant capacity of caffeic acid was inhibited by milk protein to different degrees.

[A comparison of different treatment conditions on the conformation changes of bovine lactoferrin].

In this study, the tertiary, secondary structures and disulfide bond changes of bovine lactoferrin (bLF) under 6 differents physico-chemical treatments were investigated by fluorescence, circular dichroism(CD) and UV-Vis absorption. A red shift from 333 to 354 nm in the fluorescence emission maximum (lambda(max)) was observed in the bLF treated by 6 mol x L(-1) GdnHCl, 8 mol x L(-1) Urea and 50 mmol x L(-1) DTT simultaneously, meanwhile a large number of exposed hydrophobic groups were detected. However, there was no marked shift in lambda(max) of bLF treated by heating (100 degrees C, 5 min), Ultrosonic(450 W, 5 s, 6 pulses) or beta-ME (1%), of which fluorescence intensity decreased significantly compared with the untreated bLF. The results indicated that the mechanism of changes in tertiary structure of the former three methods were different from the latter three. The detection by CD showed that the alpha- helix structure vanished completely in the bLF treated by GdnHCl. However, there was no remarkable change in the secondary structure of the bLF treated by the other five methods. In addition, UV-Vis absorption suggested that disulfide bond was seriously destructed in the bLF treated by DTT and Ultrosonict, but GdnHCl, beta-ME and heating induced a little damage merely. This study is instructive and meaningful to the further research on relationship between structure and activity of bLF.

[A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine].

In near infrared spectral quantitative analysis, the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models. However, the number of samples that can obtain accurately their chemical values is few. Many models exclude the amount of samples without chemical values, and consider only these samples with chemical values when modeling sample compositions' contents. To address this problem, a semi-supervised LS-SVR (S2 LS-SVR) model is proposed on the basis of LS-SVR, which can utilize samples without chemical values as well as those with chemical values. Similar to the LS-SVR, to train this model is equivalent to solving a linear system. Finally, the samples of flue-cured tobacco were taken as experimental material, and corresponding quantitative analysis models were constructed for four sample compositions' content(total sugar, reducing sugar, total nitrogen and nicotine) with PLS regression, LS-SVR and S2 LS-SVR. For the S2 LS-SVR model, the average relative errors between actual values and predicted ones for the four sample compositions' contents are 6.62%, 7.56%, 6.11% and 8.20%, respectively, and the correlation coefficients are 0.974 1, 0.973 3, 0.923 0 and 0.948 6, respectively. Experimental results show the S2 LS-SVR model outperforms the other two, which verifies the feasibility and efficiency of the S2 LS-SVR model.

Spectral target-detecting system using sine-wave modulation.

Target detection is one of the key technology of precision chemical application. Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China. It was difficult to adjust the output power, and the anti-interference ability was weak in these systems. In order to resolve these problems, the target detection method based on analog sine-wave modulation was studied. The spectral detecting system was set up in the aspects of working principle, electric circuit, and optical path. Lab testing was performed. The results showed that the reflected signal from the target varied inversely with detection distances. It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology. Furthermore, quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system. The research laid the foundation for the future development of the corresponding instrument.

[Near-infrared spectroscopy quantitative analysis model based on inverse regression].

In the present paper, an inverse regression method is used in near infrared (NIR) spectroscopy analysis to reduce dimension of predictor at first, then estimate linear regression function using the new derived low dimensional data. A real data set of 103 corn samples was used for analysis with this new inverse regression method. Taking 103 corn samples as experiment materials, seventy samples were chosen randomly to establish predicting model, the remaining thirty-three corn samples were viewed as prediction set. The new derived model is used to the prediction set. The coefficient is 0.986 and the average relative error is 2.1% between the model predication results and Kjeldahl's value for the protein content, and the resulis of using partial least square regression are 0.978 and 2.5%, respectively. The results demonstrate that the inverse regression method is feasible and has good property in near-infrared spectroscopy quantitative analysis, and also provides a new idea for chemometrics quantitative analysis.

[Multi-task least-squares support vector regression machines and their applications in NIR spectral analysis].

In near infrared spectral quantitative analysis, many models consider separately each component when modeling sample composition content, disregarding the underlying relatedness among sample compositions. To address this problem, the present paper views modeling each sample composition content as a task, thus one can transform the problem that models simultaneously analyze all sample compositions' contents to a multi-task learning problem. On the basis of the LS-SVR, a multi-task LS-SVR (MTLS-SVR) model is proposed. Furthermore, an efficient large-scale algorithm is given. The broomcorn samples are taken as experimental material, and corresponding quantitative analysis models are constructed for three sample composition contents (protein, lysine and starch) with LS-SVR, PLS, multiple dependent variables LS-SVR (MLS-SVR) and MTLS-SVR For the MTLS-SVR model, the average relative errors between actual values and predicted ones for the three sample compositions contents are 1.52%, 3.04% and 1.01%, respectively, and the correlation coefficients are 0.993 1, 0.894 0 and 0.940 6, respectively. Experimental results show MTLS-SVR model outperforms significantly the three others, which verifies the feasibility and efficiency of the MTLS-SVR model.

[Interaction of lactoferrin and its peptides with DPPC and DPPG liposomes studied by Raman spectroscopy].

Interaction of lactoferrin and its peptides LfcinB4-14 and LfampinB with dipalmitoylglycero-phosphocholine (DPPC) and dipalmitoylglycero-phosphoglycerol (DPPG) liposomes were studied by means of Raman spectroscopy. In our study, conformational changes in the phospholipid molecules were investigated by measuring the intensities of 2 847 and 2 882 cm(-1) Raman bands which are assigned to acyl chains' symmetric and asymmetric C-H stretching vibrations. The addition of lactoferrin and its peptides LfcinB4-14 and LfampinB caused a decrease in the 2 882 cm(-1) intensity of DPPG liposomes, thus the order parameter for the lateral interactions between chains S(lat) decreased from 0.19 to 0.17, 0.14 and 0.12 respectively. On the contrary, the intensities at 2 847 and 2 882 cm(-1) of DPPC liposomes were poorly affected by lactoferrin and its peptides. The results show that lactoferrin and its peptides present a stronger effect on the molecular structure and order degree of anionic lipid DPPG than that of zwitterionic lipid DPPC. This suggests that lactoferrin, LfcinB4-14 and LfampinB can interact and combine with the negatively charged DPPG liposomes by electrostatic interaction and perform its antibacterial activity. Besides, LfcinB4-14 and LfampinB can affect the lipid more strongly than lactoferrin.

The secondary structure of heated whey protein and its hydrolysates antigenicity.

Fourier transform infrared spectroscopy (FTIR) and circular dichroism (CD) were used to investigate the conformational changes of heated whey protein (WP) and the corresponding changes in the hydrolysates immunoreactivity were determined by competitive enzyme-linked immunosorbent assay (ELISA). Results showed that the contents of alpha- helix and beta-sheet of WP did not decrease much under mild heating conditions and the antigenicity was relatively high; when the heating intensity increased (70 degrees for 25 min or 75 degrees C for 20 min), the content of alpha- helix and beta-sheet decreased to the minimum, so was the antigenicity; However, when the WP was heated at even higher temperature and for a longer time, the beta-sheet associated with protein aggregation begun to increase and the antigenicity increased correspondingly. It was concluded that the conformations of heated WP and the antigenicity of its hydrolysates are related and the optimum structure for decreasing the hydrolysates antigeniity is the least content of alpha-helix and beta-sheet. Establishing the relationship between the WP secondary structure and WP hydrolysates antigenicity is significant to supply the reference for antigenicity reduction by enzymolysis.

FTIR analysis of protein secondary structure in cheddar cheese during ripening.

Proteolysis is one of the most important biochemical reactions during cheese ripening. Studies on the secondary structure of proteins during ripening would be helpful for characterizing protein changes for assessing cheese quality. Fourier transform infrared spectroscopy (FTIR), with self-deconvolution, second derivative analysis and band curve-fitting, was used to characterize the secondary structure of proteins in Cheddar cheese during ripening. The spectra of the amide I region showed great similarity, while the relative contents of the secondary structures underwent a series of changes. As ripening progressed, the alpha-helix content decreased and the beta-sheet content increased. This structural shift was attributed to the strengthening of hydrogen bonds that resulted from hydrolysis of caseins. In summary, FTIR could provide the basis for rapid characterization of cheese that is undergoing ripening.