Journal of Innovative Optical Health Sciences

Noninvasive detection of body composition plays a significant role in the improvement of life quality and reduction in complications of the patients, and the near-infrared (NIR) spectroscopy, with the advantages of painlessness and convenience, is considered as the most promising tool for the online noninvasive monitoring of body composition. However, quite different from other fields of online detection using NIR spectroscopy, such as food safety and environment monitoring, noninvasive detection of body composition demands higher precision of the instruments as well as more rigorousness of measurement conditions. Therefore, new challenges emerge when NIR spectroscopy is applied to the noninvasive detection of body composition, which, in this paper, are first concluded from the aspects of measurement methods, measurement conditions, instrument precision, multi-component influence, individual difference and novel weak-signal extraction method based on our previous research in the cutting-edge field of NIR noninvasive blood glucose detection. Moreover, novel ideas and approaches of our group to solve these problems are introduced, which may provide evidence for the future development of noninvasive blood glucose detection, and further contribute to the noninvasive detection of other body compositions using NIR spectroscopy.

Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges.

Watercore and sugar content are internal qualities which are impossible for exterior determination. Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectroscopy (NIR) technique. A total of 93 samples of Asian pear variety "SH-078" were used. For sugar content, spectrum of each fruit was measured in the short wavelength region (700–1100 nm) in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer. Prediction equation was performed by multiple linear regression. The result showed Standard Error of Prediction (SEP) = 0.58°Bx, and Bias=0.11. The result from t-test showed that sugar content predicted by NIR was not significantly different from the value analyzed by refractometer at 95% confidence. For watercore disorder, NIR measurement was performed over the short wavelength range (700–850 nm) in the transmission mode. The first derivative spectra were correlated with internal qualities. Then principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to perform discrimination models. The accuracy of the PCA model was greater than the PLSDA one. The scores from PC1 were separated into two boundaries, one predicted rejected pears with 100% classification accuracy, and the other was accepted pears with 92% accuracy. The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.

The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared (FT–NIR) Spectrometer. Samples of milled: parboiled rice, white rice, new Jasmine rice (harvested in 2012) and aged Jasmine rice (harvested in 2006 or during the period 2007–2011) were used in this study. The eating quality of the cooked rice, i.e., adhesiveness, hardness, dryness, whiteness and aroma, were evaluated by trained sensory panelists. FT–NIR spectroscopy models for predicting the eating quality of cooked rice were established using the partial least squares regression. Among the eating quality, the stickiness model indicated its highest prediction ability (i.e., ; RMSEP = 0.65; Bias = 0.00; RPD = 1.87) and SEP/SD of 2. In addition, it was clear that the water content did not affect the eating quality of cooked rice, rather the main chemical component implicated was starch.

Homogeneity of powder blend is essential to obtain uniform contents for the tablets and capsules. Near-infrared (NIR) spectroscopy with fiber-optic probe was used as an on-line technique for monitoring the homogeneity of pharmaceutical blend during the blending process instead of the traditional techniques, such as high performance liquid chromatograph (HPLC) method. In this paper NIRS with a SabIR diffuse reflectance fiber-optic probe was used to monitor the blending process of coptis powder and lactose (excipient) with different contents, and further qualitative methods, like similarity, moving block of standard deviation and mean square were used for calculation purposes with the collected spectra after the pretreatment of multiplicative signal correction (MSC) and second derivative. Correlation spectrum was used for the wavelength selection. Four different coptis were blended with lactose separately to validate the proposed method, and the blending process of "liu wei di huang" pill was also simulated in bottles to verify this method on multiple herbal blends. The overall results suggest that NIRS is a simple, effective and noninvasive technique can be successfully applied to the determination of homogeneity in the herbal blend.

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm^-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R²) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value.

Hyaluronic acid (HA) concentration is an important parameter in fermentation process. Currently, carbazole assay is widely used for HA content determination in routine analysis. However, this method is time-consuming, environment polluting and has the risk of microbial contamination, as well as the results lag behind fermentation process. This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared (NIR) spectroscopy in transmission mode. In this work, a total of 56 samples of fermentation broth from 7 batches were analyzed, which contained HA in the range of 2.35–9.69 g/L. Different data preprocessing methods were applied to construct calibration models. The final optimal model was obtained with first derivative using Savitzky–Golay smoothing (9 points window, second-order polynomial) and partial least squares (PLS) regression with leave-one-block-out cross validation. The correlation coefficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L, respectively, which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.

Perovskite lead zirconate (PbZrO₃) was synthesized in an orthorhombic form at a temperature below the Curie temperature, T_C. The orthorhombic form is a noncentrosymmetric structure which is capable of spontaneous polarization. Fourier transform infrared (FTIR) spectra and X-ray diffraction (XRD) patterns confirm the successful synthesis of the lead zirconate; and scanning electron microscopy (SEM) micrographs indicate that PbZrO₃ particles are moderately dispersed in the natural rubber (NR) matrix. Without an electrical field, the particles merely act as a ferroelectric filler, which can absorb and store additional stress. Under an electrical field, particle-induced dipole moments are generated, leading to interparticle interaction and a substantial increase in the storage modulus. At a small amount of lead zirconate particulates present in the natural rubber matrix, at a volume fraction of 0.007306, the electrical conductivity increases dramatically by nearly two orders of magnitude at the electrical frequency of 500 kHz.

Near infrared spectroscopy (NIRS) is based on molecular overtone and combination vibrations. It is difficult to assign specific features under complicated system. So it is necessary to find the relevance between NIRS and target compound. For this purpose, the chondroitin sulfate (CS) ethanol precipitation process was selected as the research model, and 90 samples of 5 different batches were collected and the content of CS was determined by modified carbazole method. The relevance between NIRS and CS was studied throughout optical pathlength, pretreatment methods and variables selection methods. In conclusion, the first derivative with Savitzky–Golay (SG) smoothing was selected as the best pretreatment, and the best spectral region was selected using interval partial least squares (iPLS) method under 1 mm optical cell. A multivariate calibration model was established using PLS algorithm for determining the content of CS, and the root mean square error of prediction (RMSEP) is 3.934 g⋅L^-1. This method will have great potential in process analytical technology in the future.

Near infrared spectroscopy (NIRS), coupled with principal component analysis and wavelength selection techniques, has been used to develop a robust and reliable reduced-spectrum classification model for determining the geographical origins of Nanfeng mandarins. The application of the changeable size moving window principal component analysis (CSMWPCA) provided a notably improved classification model, with correct classification rates of 92.00%, 100.00%, 90.00%, 100.00%, 100.00%, 100.00% and 100.00% for Fujian, Guangxi, Hunan, Baishe, Baofeng, Qiawan, Sanxi samples, respectively, as well as, a total classification rate of 97.52% in the wavelength range from 1007 to 1296 nm. To test and apply the proposed method, the procedure was applied to the analysis of 59 samples in an independent test set. Good identification results (correct rate of 96.61%) were also received. The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model (290 variables) into account. The results of the study showed the great potential of NIRS as a fast, nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classification of Nanfeng mandarins.

The human visual sensitivity to the flickering light has been under investigation for decades. The finding of research in this area can contribute to the understanding of human visual system mechanism and visual disorders, and establishing diagnosis and treatment of diseases. The aim of this study is to investigate the effects of the flickering light to the visual cortex by monitoring the hemodynamic responses of the brain with the functional near infrared spectroscopy (fNIRS) method. Since the acquired fNIRS signals are affected by physiological factors and measurement artifacts, constrained independent component analysis (cICA) was applied to extract the actual fNIRS responses from the obtained data. The experimental results revealed significant changes (p < 0.0001) of the hemodynamic responses of the visual cortex from the baseline when the flickering stimulation was activated. With the uses of cICA, the contrast to noise ratio (CNR), reflecting the contrast of hemodynamic concentration between rest and task, became larger. This indicated the improvement of the fNIRS signals when the noise was eliminated. In subsequent studies, statistical analysis was used to infer the correlation between the fNIRS signals and the visual stimulus. We found that there was a slight decrease of the oxygenated hemoglobin concentration (about 5.69%) over four frequencies when the modulation increased. However, the variations of oxy and deoxy-hemoglobin were not statistically significant.

The application to detect illegally added drugs in dietary supplements by near-infrared spectral imaging was studied with the focus on nifedipine, diclofenac and metformin. The method is based on near-infrared spectral images correlation coefficient to detect illegally added drugs. The results comply 100% with HPLC methods test results with no false positive results.

Samples of preparations contaminated by diethylene glycol (DEG), diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra. Then the identification models were developed using the collected spectra and the spectra of distilled water, propylene glycol and the preparations without diethylene glycol. Besides, the quantification model was also established for determining the concentration of diethylene glycol in the preparations. Validation results show the identification and quantification models have ideal prediction performance. The emergency NIR models are rapid, easy to use and accurate, and can be implemented for identifying diethylene glycol raw material, screening the preparations contaminated by diethylene glycol in the markets and analyzing the concentrations of DEG.