Two nondestructive methods based on visible and near-infrared (VIS-NIR) spectroscopy and X-ray image have been used for the evaluation of watermelon quality. The prediction performance based on partial least squares (PLS) by diffuse transmittance measurement (500–1010 nm) was evaluated for chemical quality attributes SSC (Rc = 0.903; RMSEC = 0.572% Brix; Rp = 0.862; RMSEP = 0.717% Brix; RPD = 1.83), lycopene (Rc = 0.845; RMSEC = 0.266 mg/ 100gFW; Rp = 0.751; RMSEP = 0.439 mg/100gFW; RPD = 1.13) and moisture (Rc = 0.917; RMSEC = 0.280%; Rp = 0.937; RMSEP = 0.276%; RPD = 2.79). The X-ray calibration linear equations developed by extracting the appropriate gray threshold were sufficiently precise for volume (R² = 0.986) and weight (R² = 0.993). In order to optimize prediction model of watermelon quality in growth period, multivariate multi-block technique factor analysis enabled integration of these traits: chemical information is related to physical information. Applying principle component analysis to extract common factors and varimax with Kaiser normalization to improve explanatory, the comprehensive indicator based on variances was established satisfactorily with Rc = 0.94, RMSEC = 0.244, Rp = 0.93, RMSEP = 0.344 and RPD = 2.00. A comparison of these models indicates that the comprehensive indicator determined only by portable VIS-NIR spectrometer appears as a suitable method for appraising watermelon quality nondestructively on the plant at different ripen stages. This method contributes to infer the picking date of watermelon with higher accuracy and bigger economic benefits than that by experience.

A new strategy for quantitative analysis of a major clinical biochemical indicator called glycated hemoglobin (HbA1c) was proposed. The technique was based on the simultaneous near-infrared (NIR) spectral determination of hemoglobin (Hb) and absolute HbA1c content (Hb · HbA1c) in human hemolysate samples. Wavelength selections were accomplished using the improved moving window partial least square (MWPLS) method for stability. Each model was established using an approach based on randomness, similarity, and stability to obtain objective, stable, and practical models. The optimal wavebands obtained using MWPLS were 958 to 1036nm for Hb and 1492 to 1858 nm for Hb · HbA1c, which were within the NIR overtone region. The validation root mean square error and validation correlation coefficients of prediction (V -SEP, V -R_P) were 3.4 g L^-1 and 0.967 for Hb, respectively, whereas the corresponding values for Hb · HbA1c were 0.63 g L^-1 and 0.913. The corresponding V -SEP and V -R_P were 0.40% and 0.829 for the relative percentage of HbA1c. The experimental results confirm the feasibility for the quantification of HbA1c based on simultaneous NIR spectroscopic analyses of Hb and Hb · HbA1c.

A discriminant analysis technique using wavelet transformation (WT) and influence matrix analysis (CAIMAN) method is proposed for the near infrared (NIR) spectroscopy classifi- cation. In the proposed methodology, NIR spectra are decomposed by WT for data compression and a forward feature selection is further employed to extract the relevant information from the wavelet coefficients, reducing both classification errors and model complexity. A discriminant-CAIMAN (D-CAIMAN) method is utilized to build the classification model in wavelet domain on the basis of reduced wavelet coefficients of spectral variables. NIR spectra data set of 265 salviae miltiorrhizae radix samples from 9 different geographical origins is used as an example to test the classification performance of the algorithm. For a comparison, k-nearest neighbor (KNN), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods are also employed. D-CAIMAN with wavelet-based feature selection (WD-CAIMAN) method shows the best performance, achieving the total classification rate of 100% in both cross-validation set and prediction set. It is worth noting that the WD-CAIMAN classifier also shows improved sensitivity, selectivity and model interpretability in the classifications.

In this research, suitable imaging methods were used for acquiring single compound images of biology samples of chicken pectorales tissue section, tobacco dry leaf, fresh leaf and plant glandular hair, respectively. The adverse effects caused by the high water content and the thermal effect of near infrared (NIR) light were effectively solved during the experiment procedures and the data processing. PCA algorithm was applied to the NIR micro-image of chicken pectorales tissue. Comparing the loading vector of PC3 with the NIR spectrum of dry albumen, the information of PC3 was confirmed to be provided mainly by protein, i.e., the 3rd score image represents the distribution trend of protein mainly. PCA algorithm was applied to the NIR microimage of tobacco dry leaf. The information of PC2 was confirmed to be provided by carbohydrate including starch mainly. Compared to the 2nd score image of tobacco dry leaf, the compared correlation image with the reference spectrum of starch had the same distribution trend as the 2nd score image. The comparative correlation images with the reference spectra of protein, glucose, fructose and the total plant alkaloid were acquired to confirm the distribution trend of these compounds in tobacco dry leaf respectively. Comparative correlation images of fresh leaf with the reference spectra of protein, starch, fructose, glucose and water were acquired to confirm the distribution trend of these compounds in fresh leaf. Chemimap imaging of plant glandular hair was acquired to show the tubular structure clearly.

A rapid quantitative analytical method for three components of Lonicerae Japonicae Flos solution (Lonicera Japonica Thumb.) extracted by water was developed using near-infrared (NIR) spectroscopy and the partial least-squares (PLS) method. The NIR spectra of 81 samples collected from a production line were obtained. The concentrations of secologanic acid, chlorogenic acid and galuteolin were determined by using high-performance liquid chromatography-diode array detection as the reference method. Several pretreatment methods for the NIR spectra were used during PLS calibration. The most appropriate latent variable number of the PLS factor was selected based on the standard error of cross-validation (SECV). The performance of the final PLS models was evaluated according to SECV, standard error of prediction (SEP) and determination coefficient (R²). The compounds secologanic acid, chlorogenic acid and galuteolin had SEP values of 0.030, 0.061 and 1.668 μg/mL, respectively and R² values over 0.85. This work shows that NIR spectroscopy is a rapid and convenient method for the analysis of Lonicerae Japonicae Flos solution extracted by water. The proposed method can help in the application of process analytical technology in the pharmaceutical industry, particularly in traditional Chinese medicine injections.

Moisture content is an important trait for rubber sheet trading system. Therefore, a calibration equation for predicting moisture content was created by near infrared (NIR) technique in order to develop a more fair trading system in Thailand. Spectra were recorded in two systems. One was measurement on each rubber sheet and the other was on a pile of sheets. Both were measured by a handheld NIR spectrometer in the short wavelength region (700–1100 nm) in the transflectance mode using Teflon as a diffuse reflector. The spectra showed the peak at about 900 nm which belongs to isoprene, the major component of rubber sheet. Pretreatment with second derivative was applied to remove baseline shift effect occurring due to thickness differences on each rubber sheet. From validation results, moisture contents predicted by single sheet system were more accurate than a pile of sheet system with standard error of prediction (SEP)=0.39% and bias of -0.07%, and they were not significantly different from the actual values at 95% confidence. As a result, determining moisture content in each rubber sheet by a handheld NIR spectrometer provided accurate values, easy and rapid operation.

The selection of stable wavebands for the near-infrared (NIR) spectroscopic analysis of total nitrogen (TN) in soil was accomplished by using an improved moving window partial least squares (MWPLS) method. A new modeling approach was performed based on randomness, similarity and stability, which produced an objective, stable and practical model. Based on the MWPLS method, a search was in the overall scanning region from 400 to 2498 nm, and the optimal waveband was identified to be 1424 to 2282 nm. A model space that includes 41 wavebands that are equivalent to the optimal waveband was then proposed. The public range of the 41 equivalent optimal wavebands was 1590 to 1870 nm, which contained sufficient TN information. The wavebands of 1424 to 2282 nm, 1590 to 1870 nm, and the long-NIR region 1100 to 2498 nm all achieved satisfactory validation effects. However, the public waveband of 1590 to 1870 nm had only a minimum number of wavelengths, which significantly reduced the method complexity. Various equivalent wavebands serve as guidelines for designing spectroscopic instruments. These wavebands could address the restrictions of position and the number of wavelengths in instrument design.

Near infrared microscopy imaging offers the opportunity to explore not only what kinds of chemical species are present at micro-scale level but also where the chemical species would be present. By revealing the spectral and spatial information, the technique can identify and localize any interested component. This study investigates the feasibility of using Near infrared microscopy imaging to detect melamine in soybean meal. The results showed that 6805 cm^-1 is very sensitive for melamine but not for soybean meal, so can be used for univariate analysis. Single wavelength image and peak integration image at 6805 cm^-1 are simple and effective methods to detect the melamine in soybean meal. Furthermore, Principal Component Analysis is applied to detect the melamine in soybean meal.

Thai rice is favored by large numbers of consumers of all continents because of its excellent taste, fragrant aroma and fine texture. Among all Thai rice varieties, Thai Hommali rice is the most preferred. Classification of rice as premium quality requires that almost all grain kernels of the rice be perfectly whole with only a small quantity of foreign particles. Of all the foreign particles found in rice, rice weevils can wreck severest havoc on the quality and quantity of rice such that premium grade rice is transformed into low grade rice. It is widely known that rice millers adopt the \overdose" fumigation practice to control the birth and propagation of rice weevils, the practice of which inevitably gives rise to pesticide residues on rice which end up in the body of consumers. However, if population concentration of rice weevils could be approximated, right amounts of chemicals for fumigation would be applied and thereby no overdose is required. The objective of this study is thus to estimate the quantity of rice weevils in both milled rice and brown rice of Thai Hommali rice variety using the near infrared spectroscopy (NIRS) technique. Fourier transforms near infrared (FT-NIR) spectrometer was used in this research and the near-infrared wavelength range was 780–2500 nm. A total of 20 levels of rice weevil infestation with an increment of 10 from 10 to 200 mature rice weevils were applied to 1680 rice samples. The spectral data and quantity of weevils are analyzed by partial least square regression (PLSR) to establish the model for prediction. The results show that the model is able to estimate the quantity of weevils in milled Hommali rice and brown Hommali rice with high R²_val of 0.96 and 0.90, high RPD of 6.07 and 3.26 and small bias of 2.93 and 2.94, respectively.

Germinated brown rice (GBR) is rich in gamma oryzanol which increase its consumption popularity, particularly in the health food market. The objective of this research was to apply the near infrared spectroscopy (NIRS) for evaluation of gamma oryzanol of the germinated brown rice. The germinated brown rice samples were prepared fromgerminated rough rice (soaked for 24 and 48 h, incubated for 0, 6, 12, 18, 24, 30 and 36 h) and purchased fromlocal supermarkets. The germinated brown rice samples were subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extraction methodology. The prediction model was established by partial least square regression (PLSR) and validated by full cross validation method. The NIRS model established from various varieties of germinated brown rice bought from different markets by first derivativestvector normalization pretreated spectra showed the optimal prediction with the correlation of determination (R²), root mean squared error of cross validation (RMSECV) and bias of 0.934, 8.84 × 10^-5 mg/100 g dry matter and 1.06 × 10^-5 mg/100 g dry matter, respectively.This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice. This information is very useful to the germinated brown rice production factory and consumers.

Two universal spectral ranges (4550–4100 cm^-1 and 6190–5510 cm^-1) for construction of quantitative models of homologous analogs of cephalosporins were proposed by evaluating the performance of five spectral ranges and their combinations, using three data sets of cephalosporins for injection, i.e., cefuroxime sodium, ceftriaxone sodium and cefoperazone sodium. Subsequently, the proposed ranges were validated by using eight calibration sets of other homologous analogs of cephalosporins for injection, namely cefmenoxime hydrochloride, ceftezole sodium, cefmetazole, cefoxitin sodium, cefotaxime sodium, cefradine, cephazolin sodium and ceftizoxime sodium. All the constructed quantitative models for the eight kinds of cephalosporins using these universal ranges could fulfill the requirements for quick quantification. After that, competitive adaptive reweighted sampling (CARS) algorithm and infrared (IR)–near infrared (NIR) two-dimensional (2D) correlation spectral analysis were used to determine the scientific basis of these two spectral ranges as the universal regions for the construction of quantitative models of cephalosporins. The CARS algorithm demonstrated that the ranges of 4550–4100 cm^-1 and 6190–5510 cm^-1 included some key wavenumbers which could be attributed to content changes of cephalosporins. The IR–NIR 2D spectral analysis showed that certain wavenumbers in these two regions have strong correlations to the structures of those cephalosporins that were easy to degrade.

Near Infrared spectroscopy (NIRS) has been widely used in the discrimination (classification) of pharmaceutical drugs. In real applications, however, the class imbalance of the drug samples, i.e., the number of one drug sample may be much larger than the number of the other drugs, deceases drastically the discrimination performance of the classification models. To address this class imbalance problem, a new computational method — the scaled convex hull (SCH)-based maximum margin classifier is proposed in this paper. By a suitable selection of the reduction factor of the SCHs generated by the two classes of drug samples, respectively, the maximal margin classifier between SCHs can be constructed which can obtain good classification performance. With an optimization of the parameters involved in the modeling by Cuckoo Search, a satisfied model is achieved for the classification of the drug. The experiments on spectra samples produced by a pharmaceutical company show that the proposed method is more effective and robust than the existing ones.