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Background: Metabolic syndrome (MS) is one of the major causes of coronary artery diseases (CAD). Gut microbiome diversity and its natural fermentation products are not only correlated with MS and CAD, but their correlations also appear to be stronger than the associations with traditional risk factors. Therefore, the aim of this study was to provide a new potential pathway for the natural fermentation product butyrate to improve MS and to examine whether it is associated with serum metabolic profiles and gut flora composition. Methods: C57BL/6J mice fed a high-fat diet (HFD) were treated with 400 mg/kg of sodium butyrate for 16 weeks. Blood and fecal samples were collected, and the metabolite concentrations and 16s rRNA were measured with liquid chromatography–MS and Illumina platform, respectively. The plasma differential metabolites and gut microbiome composition were analyzed with XCMS online and QIIME 2, respectively. Results: Gut microbiome-derived butyrate reduced glucose intolerance and insulin resistance, resisting HFD-induced increase in the relative abundance of f_Lachnospiraceae, f_Rikenellaceae, and f_Paraprevotellaceae. Meanwhile, sodium butyrate increased the levels of α-linolenate, all-trans-retinal, resolvin E1, and leukotriene in the plasma, and the differential pathways showed enrichment in mainly resolvin E biosynthesis, histidine degradation, lipoxin biosynthesis, and leukotriene biosynthesis. Moreover, sodium butyrate increased the levels of phosphorylated-adenosine 5′-monophosphate-activated protein kinase (p-AMPK) and facilitated glucose transporter member 4 (GLUT4) in the adipose tissue. Conclusion: Butyrate can induce AMPK activation and GLUT4 expression in the adipose tissue, improving cardiovascular disease (CVD)-related metabolic disorder, resisting HFD-induced gut microbiome dysbiosis, and promoting resolvin E1 and lipoxin biosynthesis. Oral supplement of the natural fermentation product butyrate can be a potential strategy for preventing CVD.

Whole blood viscosity (WBV) is a group of important clinical indicators of cardio-cerebral vascular diseases. Existing detection methods for WBV are complex, making them inconvenient for large population screening. Blood viscosity is closely related to the deformability and aggregation of erythrocytes, which are associated with haemoglobin. Haemoglobin has obvious near-infrared (NIR) spectral absorption. Scattering occurs when NIR light enters a viscous blood sample, and its scattering degree is correlated with blood viscosity. Based on repeated spectral measurements and spectral similarity, spectral noise-to-signal ratio (NSR) was proposed to quantify the spectral scattering effect in the blood sample. A novel selection method of piecewise-continuous wavelengths, named NSR priority-partial least squares (NSRP-PLS), was proposed and applied for visible-NIR quantitative analysis of WBV with high, medium and low shear rates [WBV(H), WBV(M), WBV(L)]. Modelling was separately performed by gender to allow for systematic gender differences in blood viscosity. For the NIR-predicted and clinically measured values of the three WBV indicators in independent validation, the root mean square errors for prediction (SEP) were 0.498, 0.222 and 0.193 (mPa·s), respectively. And the correlation coefficients (RP) were 0.927, 0.934 and 0.927, respectively. Compared with the three current well-performing methods (MW-PLS, CARS-PLS and SPA-PLS), the proposed NSRP-PLS method achieved better predictive accuracy. Results indicated that visible-NIR spectroscopy combined with the NSRP-PLS method can be used for the quantitative analysis of WBV. The proposed analytical method is rapid, reagent-free and is scientific and meaningful for cardio-cerebral vascular diseases screening in large populations.

The rapid and non-destructive discriminant analysis of rice seeds has great significance for large-scale agriculture. Using near-infrared (NIR) diffuse-reflectance spectroscopy with partial least squares-discriminant analysis (PLS-DA), a variety identification method of multi-grain rice seeds was developed. The equidistant combination method was adopted for large-range wavelength screening. A step-by-step phase-out method was proposed to eliminate interference wavelengths and improve the predicted effect. The optimal wavelength model was a combination of 54 wavelengths within 808–974 nm of the short-NIR region. One type of pure rice variety (Y Liangyou 900) was used for identification (negative). Positive samples included the other four pure varieties and contamination of Y Liangyou 900 by the above four varieties. The recognition-accuracy rates for positive, negative and total validation samples reached 93.1%, 95.1%, and 94.3%, respectively. In the long-NIR region, the local optimal wavelength model was a combination of 49 wavelengths within 1188–1650 nm, and the recognition-accuracy rates for positive, negative and total validation samples were 90.3%, 94.1%, and 92.5%, respectively. Results confirmed the feasibility of NIR spectroscopy for variety identification of multi-grain rice seeds. The proposed two discrete-wavelength models located in the short- and long-NIR regions can provide valuable reference to a dedicated spectrometer.

Using binary linear regression, the optimal partner wavelength of each wavelength is selected in an initial wavelength screening region. On the basis of strategy above, a novel approach for selecting appropriate wavelengths combination, named optimal partner wavelength combination (OPWC) coupled with partial least squares (PLS), is proposed, and was successfully applied for reagent-free near-infrared spectroscopic analysis of organic matter in soil. Moving window PLS (MW-PLS), successive projections algorithm (SPA) and Monte Carlo uninformative variable elimination (MC-UVE), which are well-performed wavelength selection methods, were also conducted for comparison. The OPWC-PLS, MW-PLS, SPA-PLS and MC-UVE-PLS methods selected 14, 210, 63, 199 wavelengths, respectively. The root-mean-square error and correlation coefficients for leave-one-out cross validation were 0.165 g kg − 1 and 0.967 for OPWC-PLS, 0.163 g kg − 1 and 0.968 for MW-PLS, 0.198 g kg − 1 and 0.953 for SPA-PLS, and 0.190 g kg − 1 and 0.956 for MC-UVE-PLS, respectively. The results indicate that OPWC-PLS and MW-PLS methods were almost the same, and were obvious better than SPA-PLS and MC-UVE-PLS methods. But the OPWC only contained 14 wavelengths, which is a high efficient approach for extracting information wavelengths and mitigating redundant wavelengths. OPWC can be also provided valuable reference for designing small dedicated spectrometers with a high signal-to-noise ratio. OPWC can be programmed determined, which has small amount of calculation and high operating speed, and it is a deterministic search technique whose results are reproducible. We believe that OPWC has such applicability and can be applied to other fields of spectroscopic analysis.

Development of small, dedicated, reagentless, and low-cost spectrometer has broad application prospects in large-scale agriculture. An appropriate wavelength selection method is a key, albeit difficult, technical aspect. A novel wavelength selection method, named equidistant combination partial least squares (EC-PLS), was applied for wavenumber selection for near-infrared analysis of crude protein, moisture, and crude fat in corn. Based on the EC-PLS, a model set that includes various models equivalent to the optimal model was proposed to select independent and joint-analyses models. The independent analysis models for crude protein, moisture, and crude fat contained only 16, 12, and 22 wavenumbers, whereas the joint-analyses model for the three indicators contained only 27 wavenumbers. Random validation samples excluded from the modeling process were used to validate the four selected models. For the independent analysis models, the validation root mean square errors (V_SEP), validation correlation coefficients (V_R P ), and relative validation root mean square errors (V_RSEP) of prediction were 0.271%, 0.946, and 2.8% for crude protein, 0.275%, 0.936, and 2.6% for moisture, and 0.183%, 0.924, and 4.5% for crude fat, respectively. For the joint-analyses model, the V_SEP, V_R P , and V_RSEP were 0.302%, 0.934, and 3.2% for crude protein, 0.280%, 0.935, and 2.7% for moisture, and 0.228%, 0.910, and 5.6% for crude fat, respectively. The results indicated good validation effects and low complexity. Thus, the established models were simple and efficient. The proposed wavenumber selection method provided also valuable reference for designing small dedicated spectrometer for corn. Moreover, the methodological framework and optimization algorithm are universal, such that they can be applied to other fields.

Teicoplanin (TCP) is a multiple drug-resistant lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. In this study, a mixture of TCP with the Tris-HCl buffer (TCP-Tris- HCl) was used to simulate TCP fermentation broth. The reagent-free, rapid and simultaneous quantitative analysis models for TCP and Tris in the TCP-Tris-HCl mixtures were established by near-infrared (NIR) spectroscopy. The equidistant combination partial least squares (EC-PLS) method and the equivalent model sets were proposed, the simplest equivalent model with the smallest number of wavelengths were further selected. The initial wavelength, number of wavelengths, number of wavelength gaps, number of PLS factors were 1520 nm, 28, 5, 5 for TCP and 1084 nm, 13, 6, 4 for Tris, respectively. Compared with the optimal EC-PLS models, the simplest equivalent models adopted fewer wavelengths. Thus, the redundant wavelengths were removed, the models were further simplified. The root-mean-square errors (SEP) and correlation coefficients (RP) for prediction were 0.043 mg·mL-1 and 0.9998 for TCP, and 0.222 mg·mL-1 and 0.9989 for Tris, respectively. The results indicate that NIR method can be applied to highly accurate quantitative analysis for TCP and provide valuable references for further application to TCP fermentation broth.

Tinnitus is a common clinical symptom. Researches have shown that fractal sound can effectively treat tinnitus. But current fractal sound is usually synthesized based on constant notes via fractal algorithm, which lead to monotony of synthesized fractal sound. So it is difficult to achieve personalized match. Clinical datas have confirmed that it is common to match tinnitus sound with nature sound and it has a good effect on regulating negative emotion and relieving tinnitus via some natural sound. Therefore, a new method of personalized synthesizing tinnitus rehabilitation sound based on iterative function system (IFS) fractal algorithm is proposed in this paper. This method firstly generates personalized audio library based on natural sound, then tinnitus rehabilitation sound is synthesized via IFS fractal algorithm. Simulation results show that rehabilitation sound in this paper can meet the basic requirements of tinnitus therapy sound and can match tinnitus sound by controlling personalized audio library. So it has reference significance to the treatment of tinnitus sound therapy.耳鸣是一种常见的临床症状。研究表明，分形音可有效治疗耳鸣，但目前的分形音多基于固定的音符库结合分形算法映射而成，其频率单一，很难与患者进行个性化适配。临床研究证实，自然声为常用的耳鸣匹配声，对调节消极情绪、减轻耳鸣的烦扰程度有良好的效果。因此，本文提出了一种基于迭代函数系统（IFS）的个性化耳鸣康复自然音合成方法。该方法以个性化构造的自然音音频库为基础，通过 IFS 分形算法映射合成康复音。实验表明，此方法合成的康复音符合耳鸣治疗声基本要求，且可通过控制音频库的生成使康复声适配患者的耳鸣声，对耳鸣治疗具有一定的参考价值。.

A novel near-infrared-spectroscopy-based quantification method for glycated hemoglobin (HbA1c), a major clinical diagnosis indicator of diabetes, was developed on the basis of simultaneous determination of hemoglobin (Hb) and absolute HbA1c content (Hb•HbA1c) in human hemolysate samples. Equidistant combination partial least squares (EC-PLS) method was proposed to perform wavelengths selection. Competitive adaptive reweighted sampling PLS (CARS-PLS) and Monte Carlo uninformative variable elimination PLS (MC-UVE-PLS) methods were also conducted for comparison. A randomness and stability dependent rigorous process of calibration, prediction, and validation was performed to produce objective and stable models. The search range covered the unsaturated region (780–1880 nm, 2090–2330 nm). For Hb and Hb•HbA1c, only 6 and 14 wavelengths were selected with EC-PLS, 23 and 30 wavelengths were selected with CARS-PLS, and 100 and 120 wavelengths were selected with MC-UVE-PLS, respectively. The predicted values of relative percentage HbA1c were calculated from the predicted Hb and Hb•HbA1c values. The sensitivity and specificity for diabetes were 93.5% and 97.1% with EC-PLS, 91.3% and 94.1% with CARS-PLS, and 89.1% and 76.5% with MC-UVE-PLS, respectively. In three methods, EC-PLS not only employed the least wavelengths but also produced the best quantification accuracy for HbA1c. EC-PLS also achieved the highest classification accuracy for negative and positive samples for diabetes. The results confirm the feasibility of HbA1c quantification based on the simultaneous analysis of Hb and Hb•HbA1c with NIR spectroscopy. This technique is rapid and simple when compared with conventional methods, and is a promising tool for screening diabetes in large populations.

A wavelength selection method for spectroscopic analysis, named correlation coefficient optimization coupled with partial least-squares (CCO-PLS), is proposed, and was successfully employed for reagent-free ATR-FTIR spectroscopic analysis of albumin (ALB) and globulin (GLB) in human serum. By varying the upper bound of correlation coefficient between absorbance and analyte's content, the CCO-PLS method achieved multi-band selection. Two PLS-based methods, which used a waveband having positive peaks of the first loading vector (FLV) and a combination of positive peaks of the correlation coefficient spectrum, were also conducted for comparison. Based on the leave-one-out cross-validation for CCO-PLS, appropriate waveband combinations for ALB and GLB were selected, the root-mean-square errors of prediction for validation samples were 1.36 and 1.35 (g L−1) for ALB and GLB, respectively, which were better than the two comparison methods. The CCO-PLS method provided a new approach for multi-band selection to achieve high analytical accuracy for molecular absorption bands that were composed of several spaced wavebands.

Near-infrared (NIR) spectroscopy combined with chemometrics was applied to rapidly analyse haemoglobin A2 (HbA2) for β-thalassemia screening in human haemolysate samples. The relative content indicator HbA2 was indirectly quantified by simultaneous analysis of two absolute content indicators (Hb and Hb∙HbA2). According to the comprehensive prediction effect of the multiple partitioning of calibration and prediction sets, the parameters were optimized to achieve modelling stability, and the preferred models were validated using the samples not involved in modelling. Savitzky-Golay smoothing was firstly used for the spectral pretreatment. The absorbance optimization partial least squares (AO-PLS) was used to eliminate high-absorption wave-bands appropriately. The equidistant combination PLS (EC-PLS) was further used to optimize wavelength models. The selected optimal models were I=856nm, N=16, G=1 and F=6 for Hb and I=988nm, N=12, G=2 and F=5 for Hb∙HbA2. Through independent validation, the root-mean-square errors and correlation coefficients for prediction (RMSEP, RP) were 3.50gL-1 and 0.977 for Hb and 0.38gL-1 and 0.917 for Hb∙HbA2, respectively. The predicted values of relative percentage HbA2 were further calculated, and the calculated RMSEP and RP were 0.31% and 0.965, respectively. The sensitivity and specificity for β-thalassemia both reached 100%. Therefore, the prediction of HbA2 achieved high accuracy for distinguishing β-thalassemia. The local optimal models for single parameter and the optimal equivalent model sets were proposed, providing more models to match possible constraints in practical applications. The NIR analysis method for the screening indicator of β-thalassemia was successfully established. The proposed method was rapid, simple and promising for thalassemia screening in a large population.

The combination optimization method of partial least squares regression combined with Savitzky-Golay smoothing was proposed, and successfully applied to near-infrared spectroscopic analysis of hemoglobin in the human peripheral blood samples.

The Savitzky–Golay (SG) method and moving-window waveband screening are applied to a coupling model of principal component (PCA) and linear discriminant analyses (LDA). An SG-pretreatment-based method (MW-PCA-LDA) for spectral pattern recognition is proposed, which is successfully employed for the non-destructive recognition of transgenic sugarcane leaves using visible (Vis) and near-infrared (NIR) diffuse reflectance spectroscopy. A Kennard–Stone-algorithm-based process of calibration, prediction and validation in consideration of uniformity and representative was performed to produce objective models. A total of 456 samples of sugarcane leaves in the elongation stage were collected from a planted field. These samples were composed of 306 transgenic samples containing both Bacillus thuringiensis (Bt) and bialaphos resistance (Bar) genes, and 150 non-transgenic samples. According to the spectral recognition effects, two parallel optimal SG modes were selected. The one of the 1st order derivative, 3rd degree polynomial and 25 smoothing points was taken as an example to pretreat the diffuse reflectance spectra. Based on the MW-PCA-LDA method, the optimal waveband was 768 nm to 822 nm, the optimal PC combination was PC1–PC3 and the corresponding validation recognition rates of transgenic and non-transgenic samples achieved 99.1% and 98.0%, respectively. The results show that Vis-NIR spectroscopy combined with SG pretreatment and the MW-PCA-LDA method can be used for accurate recognition of transgenic sugarcane leaves and provides a quick and convenient means of screening transgenic sugarcane breeding for large-scale agricultural production.

Seed purity is a crucial indicator of seed breeding, production and circulation. The traditional purity analysis methods are based on the authenticity identification of single-seed, which are complex, time consuming and lowly efficient. Using visible and near-infrared (Vis-NIR) spectroscopy, a novel purity analysis method of multi-grain rice seeds was developed. The samples were mixed samples that the target variety (Y Liangyou 900) was contaminated by the other four varieties. The standard normal variate method was used for spectral pretreatment. The equidistant combination-partial least squares (EC-PLS) was adopted for large-range wavelength screening. The wavelength step-by-step phase-out PLS (WSP-PLS) was further used to eliminate interference wavelengths and improve predicted effect. In vis-short NIR region, the selected WSP-PLS model included 19 non-equidistant wavelengths. The root mean square errors and correlation coefficients for prediction in modeling (RMSEP M , R P,M ) reached 0.115, 0.920, respectively. In validation, the root mean square errors and correlation coefficients for prediction (RMSEP V , R P,V ) were 0.152, 0.845, respectively. In the long NIR region, the selected model included 24 non-equidistant wavelengths, the RMSEP M , R P,M were 0.103, 0.930 and the RMSEP V , R P,V were 0.129, 0.894, respectively. Results showed that the predicted and actual purity values had high correlation, and the wavelength model complexity was low. The proposed Vis-NIR detection method has the feasibility to analyse the purity of multi-grain rice seeds, which is a rapid, non-destructive and promising analytical technique. As a simple and efficient wavelength selection method, WSP-PLS is expected to be used for more analysis objects.

A rapid analytical method of human whole blood viscosity with low, medium, and high shear rates [WBV(L), WBV(M), and WBV(H), respectively] was developed using visible and near-infrared (Vis-NIR) spectroscopy combined with a moving-window partial least squares (MW-PLS) method. Two groups of peripheral blood samples were collected for modeling and validation. Separate analytical models were established for male and female groups to avoid interference in different gender groups and improve the homogeneity and prediction accuracy. Modeling was performed for multiple divisions of calibration and prediction sets to avoid over-fitting and achieve parameter stability. The joint analysis models for three indicators were selected through comprehensive evaluation of MW-PLS. The selected joint analysis models were 812–1278 nm for males and 670–1146 nm for females. The root-mean-square errors (SEP) and the correlation coefficients of prediction (RP) for all validation samples were 0.54 mPa•s and 0.91 for WBV(L), 0.25 mPa•s and 0.92 for WBV(M), and 0.22 mPa•s and 0.90 for WBV(H). Results indicated high prediction accuracy, with prediction values similar to the clinically measured values. Overall, the findings confirmed the feasibility of whole blood viscosity quantification based on Vis-NIR spectroscopy with MW-PLS. The proposed rapid and simple technique is a promising tool for surveillance, control, and treatment of cardio-cerebrovascular diseases in large populations.

Tinnitus is a common clinical symptom and its occurrence rate is high. It seriously affects life quality of the patients. Scientific researches show that listening some similar and none-repetitive music can relieve tinnitus to some extent. The overall music accorded with self-similarity character by the direct mapping method based on chaos. However, there were often the same tones continuous repeating a few times and tone mutations. To solve the problem, this paper proposes a new method for tinnitus rehabilitation sound synthesis based on pentatonic scale, chaos and musical instrument digital interface (MIDI). Experimental results showed that the tinnitus rehabilitation sounds were not only self-similar and incompletely reduplicate, but also no sudden changes. Thus, it has a referential significance for tinnitus treatment.

Savitzky–Golay (SG) smoothing and moving window partial least square (MWPLS) methods were applied to the model optimization and the waveband selection for near-infrared (NIR) spectroscopy analysis of soil organic matter. The optimal single wavelength prediction bias (OSWPB) was used to evaluate the similarity of calibration set and prediction set, and a new division method for calibration set and prediction set was proposed. SG smoothing modes were expanded to 540 kinds. The specific computer algorithm platforms for optimization of SG smoothing mode combined with PLS factor and for MWPLS method with changeable parameters were built up. The optimal waveband for soil organic matter was 1926–2032 nm, the optimal smoothing mode was the 2nd order derivative, 6th degree polynomial, 45 smoothing points, the PLS factor, RMSEP and R P were 8, 0.260 (%) and 0.877 respectively. The prediction effect was obviously better than that in the whole spectral collecting region. To get stable results, all the optimization processes were based on the average prediction effect on 50 different divisions of calibration set and prediction set.

The moving-window bis-correlation coefficients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and [Formula: see text]-thalassemia with visible and near-infrared (Vis–NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG) smoothing with first-order derivative ([Formula: see text]), third-degree polynomial ([Formula: see text]) and 25 smoothing points ([Formula: see text]). The selected waveband was 736–1054[Formula: see text]nm with MW-BiCC, and the positive and negative validation recognition rates ([Formula: see text]_REC[Formula: see text], [Formula: see text]_REC[Formula: see text] were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 [Formula: see text]-thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with [Formula: see text], [Formula: see text] and [Formula: see text]. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342[Formula: see text]nm). The [Formula: see text]_REC[Formula: see text] and [Formula: see text]_REC[Formula: see text] were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing [Formula: see text]-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

Liquid–liquid equilibrium tie line data were determined for three quaternary systems water + ethanol + diethyl carbonate+n-heptane, water + ethanol + 1,1-dimethylethyl methyl ether + diethyl carbonate, and water + 1,1-dimethylethyl methyl ether + diethyl carbonate+n-heptane at 298.15 K and atmospheric pressure. The experimental liquid–liquid equilibria results have been correlated using a modified UNIQUAC model and an extended UNIQUAC model, both with multicomponent interaction parameters in addition to the binary ones.

The development of a small, dedicated near-infrared (NIR) spectrometer has promising potential applications, such as for joint analyses of total cholesterol (TC) and triglyceride (TG) in human serum for preventing and treating hyperlipidemia of a large population. The appropriate wavelength selection is a key technology for developing such a spectrometer. For this reason, a novel wavelength selection method, named the equidistant combination partial least squares (EC-PLS), was applied to the wavelength selection for the NIR analyses of TC and TG in human serum. A rigorous process based on the various divisions of calibration and prediction sets was performed to achieve modeling optimization with stability. By applying EC-PLS, a model set was developed, which consists of various models that were equivalent to the optimal model. The joint analyses model of the two indicators was further selected with only 50 wavelengths. The random validation samples excluded from the modeling process were used to validate the selected model. The root-mean-square errors, correlation coefficients and ratio of performance to deviation for the prediction were 0.197 mmol L(-1), 0.985 and 5.6 for TC, and 0.101 mmol L(-1), 0.992 and 8.0 for TG, respectively. The sensitivity and specificity for hyperlipidemia were 96.2% and 98.0%. These findings indicate high prediction accuracy and low model complexity. The proposed wavelength selection provided valuable references for the designing of a small, dedicated spectrometer for hyperlipidemia. The methodological framework and optimization algorithm are universal, such that they can be applied to other fields.

Starches from a variety of Sake-rice (Yamadanishiki, Gohyakumangoku, Miyamanishiki, Kitanishiki and Wakamizu) which are suitable for Sake brewing were investigated by comparing them with Koshihikari starch to recognize their structural properties of amylopectins. The partial hydrolysate (about 15% hy drolysis) of starch with isoamylase was fractionated into three fractions (fr.1, fr.2 and fr.3) by gel permeation chromatography (GPC) with a Toyopearl HW-50S, and then the short-amylose chains derived mainly from the outer portion of the amylopectin collected in the fr.3 were analyzed by a HPAEC-system equipped with a pulsed amperometric detector (HPAEC-PAD). From comparison of the chain-length distributions between Sake-rice starches and that of Koshihikari, it was found that Sake-rice amylopectins were plentiful in longish chains around DP 13-23 and scarce in shortish chains around DP 6-12, suggesting a structural property susceptible to retrogradation. The pasting properties of Sake-rice starches analyzed by Rapid Visco Analyzer (RVA) showed higher pasting temperature (PT), higher setback (SB), and lower breakdown (BD) than Koshi hikari starch, suggesting the outer portions of amylopectin had abundant longish debranched short side chains. These results suggested that rice starches with amylopectins susceptible to retrogradation are adequate for Sake-rice.

Teicoplanin (TCP) is an important lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. The change in TCP concentration is important to measure in the fermentation process. In this study, a reagent-free and rapid quantification method for TCP in the TCP–Tris–HCl mixture samples was developed using near-infrared (NIR) spectroscopy by focusing our attention on the fermentation process for TCP. The absorbance optimization (AO) partial least squares (PLS) was proposed and integrated with the moving window (MW) PLS, which is called AO–MW–PLS method, to select appropriate wavebands. A model set that includes various wavebands that were equivalent to the optimal AO–MW–PLS waveband was proposed based on statistical considerations. The public region of all equivalent wavebands was just one of the equivalent wavebands. The obtained public regions were 1540–1868[Formula: see text]nm for TCP and 1114–1310[Formula: see text]nm for Tris. The root-mean-square error and correlation coefficient for leave-one-out cross validation were 0.046[Formula: see text]mg mL[Formula: see text] and 0.9998[Formula: see text]mg mL[Formula: see text] for TCP, and 0.235[Formula: see text]mg mL[Formula: see text] and 0.9986[Formula: see text]mg mL[Formula: see text] for Tris, respectively. All the models achieved highly accurate prediction effects, and the selected wavebands provided valuable references for designing specialized spectrometers. This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis fields.

Tinnitus is a common clinical symptom. It has a bad effect on one's hearing and sleeping, and can cause people to feel depression. Tinnitus does great harm to the patients' work and lives. In recent years, scholars focus on the research of tinnitus retraining therapy, which is on the basis of neurophysiology and psychology model. According to the surveys, it has a good effect by using some music to decompress, to regulate negative emotion, and to relieve tinnitus. Thus, patients can relax vigilance against tinnitus, break the association and the vicious cycle chain between negative emotion and tinnitus. Based upon the MIDI (Musical Instrument Digital Interface) files synthesis and the chaos theory, which is characterized by a seemingly random but an inherent and deep regulation, this paper proposes a new method which applies chaos to synthetic music, for tinnitus medical rehabilitation. Experiment results show that the proposed method can achieve good effects and has practical reference value.

A reagent-free determination method for the thalassemia screening indicators hemoglobin (Hb), mean corpuscular Hb (MCH), and mean corpuscular volume (MCV) was developed based on Fourier transform infrared spectrometers equipped with an attenuated total reflection accessory. A random and stability-dependent rigorous process of calibration, prediction, and validation was conducted. Appropriate wavebands were selected using the improved moving window partial least squares method with stability and equivalence. The obtained optimal wavebands were 1722 to 1504 cm−1 for Hb, 1653 to 901 cm−1 for MCH, and 1562 to 964 cm−1 for MCV. A model set equivalent to the optimal model was proposed for each indicator; the public waveband of Hb equivalent wavebands was 1717 to 1510 cm−1, and the public equivalent waveband for MCH and MCV was 1562 to 901 cm−1. All selected wavebands were within the MIR fingerprint region and achieved high validation effects. The sensitivity and specificity were 100.0% and 96.9% for the optimal wavebands and 100.0% and 95.3% for the equivalent wavebands, respectively. Thus, the spectral prediction was highly accurate for determining negative and positive for thalassemia screening. This technique is rapid and simple in comparison with conventional methods and is a promising tool for thalassemia screening in large populations.

Equidistant combination multiple linear regression (EC-MLR) for the quasi-continuous wavelength selection of spectroscopic analysis was proposed and successfully applied to the reagent-free determination of soil organic matter with near-infrared spectroscopy. For comparison, the continuous-mode moving window partial least squares (MWPLS) and the discrete-mode successive projections algorithm (SPA) were improved by considering the stability and applied to the same analysis object as well. All methods exhibited good effect, but the modeling accuracy, stability, and validation effect of EC-MLR were better than that of the other two methods. Compared with MWPLS, the optimal EC-MLR model contained only 16 wavelengths, and method complexity was substantially reduced. Compared with SPA-MLR, the optimal EC-MLR model could easily undergo spectral preprocessing to improve predictive capability. Moreover, appropriate equidistant discrete wavelength combination with EC-MLR corresponded to the spectral absorption band with proper resolution and can effectively overcome co-linearity interruption for the MLR model. Thus, the EC-MLR method has great potential in practical application and instrument design.

The rapid determination method of blood clinical biochemical indicators based on near infrared spectral (NIRS) analysis is an important research branch in health monitoring systems. In this paper, the rapid determination method and the optimal analysis model of serum cholesterol were established by using the NIRS technology, partial least squares (PLS) and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavenumber model, calibration set and prediction set were divided. The calibration and prediction models were established by using PLS method adopting the combination bands of 10000-5300 cm-1 and 4920-4160 cm-1 with SG smoothing. By extending the number of smoothing points to 5, 7... 61 (odd) and polynomial degree to 2, 3, 4, 5, 6, fourteen smoothing coefficient tables including 400 SG smooth modes were calculated. Based on computer algorithms platform which was developed by authors, PLS models corresponding to all combinations of 400 SG smooth modes and 1-40 PLS factors were constructed. The optimal model was selected according to the prediction effect, and the derivation order is 1, the polynomial degree is 3 or 4, the number of smoothing points is 43, the optimal PLS factor is 13, the prediction correlation coefficient RP is 0.811, and the optimal RMSEP reaches 0.416 mmol/L. The dividing method for calibration set and prediction set, the extending of SG smoothing modes, large-scale joint optimization of SG smoothing modes and PLS factors can be effectively applied to the model optimization of NIRS analysis.

The wavenumbers combination selection of near infrared spectroscopy (NIRS) analysis was very important for improving model prediction effect, reducing model complexity and designing special NIRS instruments with high signal noise ratio. Based on the prediction effect of single wavenumber linear regression model, a wavenumbers combination selection method of NIRS analysis of glucose in human serum was developed. 25 wavenumbers with good prediction effect were selected. All wavenumber combinations of the twenty-five wavenumbers were used to establish multiple linear regression (MLR) models respectively. According to the prediction effect, the optimal MLR model was the eleven wavenumbers combination of 7340, 7328, 7311, 7253, 7251, 7234, 7228, 7220, 7218, 7207, 7203 (cm-1), the corresponding root mean squared error of predication (RMSEP) was 0.384 mmol/L. And the prediction effect was obvious better than one of partial least squares (PLS) model. These indicated that the wavenumbers combination selection method based on the prediction effect of single wavenumber linear regression model could be applied to the NIRS analysis and could provide valuable reference for designing minitype special NIRS instruments.

The rapid and simple analytical method for soil organic matter by near infrared spectroscopy (NIRS) is very significant in precision agriculture. In this paper, the rapid determination method and the analysis model of soil organic matter were established by using the NIRS technology, partial least squares (PLS) regression and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavelength model, calibration set and prediction set were divided. By extending the number of smoothing points and the degree of polynomial, 483 smooth modes were calculated. The PLS models corresponding to all combinations of 483 SG smoothing modes and 1-30 PLS factor were established respectively. The optimal smoothing parameters were the second order derivative smoothing, 2 or 3 degree polynomial, 61 smoothing points, the optimal PLS factor, root mean squared error of predication (RMSEP) and correlation coefficient of predication (RP) were 19, 0.197 (%) and 0.925 respectively, which was obviously superior to the direct PLS model without SG smoothing and the optimal SG smoothing model within 25 smoothing points (the original smoothing method). This demonstrates that the extending of SG smoothing modes and large-scale simultaneous optimization selection of SG smoothing parameters and PLS factor was all very necessary, and can be effectively applied to the model optimization of NIRS analysis.

The directly rapid determination method and the optimal analysis model of hemoglobin (HGB) in human whole blood were established by using the Fourier transform infrared spectroscopy (FTIR) and attenuated total reflection (ATR) technology. Based on the prediction effect of single wavenumber linear regression model, a wavenumbers selection method of FTIR/ATR spectroscopy analysis was developed. 20 single wavenumber models which good prediction effect and the corresponding wavenumbers were selected. All wavenumber combinations of the twenty wavenumbers were used to establish multiple linear regression (MLR) models respectively. The optimal model was selected according to the prediction effect. The optimal MLR model was the eight wavenumbers combination of 2860, 2853, 2818, 1547, 1531, 1400, 1393, 1389 (cm-1), the corresponding root mean squared error of predication (RMSEP), correlation coefficient of predication (RP) and relative root mean squared error of predication (RRMSEP) were 6.63 g/L, 0.827, and 4.92% respectively. And the prediction effect was obvious better than one of partial least squares (PLS) model. These indicated that the wavenumbers selection method based on prediction effect of single wavenumber linear regression model could be applied to the FTIR/ATR spectroscopy analysis of hemoglobin in human whole blood.

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.

A rapid determination method for the preliminary thalassaemia screening indicators—haemoglobin (Hb), mean corpuscular Hb (MCH) and mean corpuscular volume (MCV)—based on near-infrared (NIR) spectroscopy was developed. Wavelength selections were accomplished using the improved moving window partial least squares (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 screened using MWPLS were 1538 nm to 1756 nm for Hb and 968 nm to 1028 nm for MCH and MCV, which were within the NIR overtone region. The validation root mean square error, validation correlation coefficients and relative validation root mean square error of prediction were 3.9 g L−1, 0.972 and 2.9% for Hb; 1.41 pg, 0.958 and 4.8% for MCH; and 4.09 fL, 0.929 and 4.9% for MCV, respectively. The three models achieved high validation accuracy, and the Hb, MCH and MCV prediction values were close to the clinical measured values. Based on the cut-off values (MCH = 27.0 pg, MCV = 80.0 fL) for the routine method, the sensitivity and specificity both reached 100% based on the spectral-predicted MCH and MCV values in the validation set. The spectral prediction was highly accurate for determining negative and positive samples in preliminary thalassaemia screening. The proposed method is rapid and simple compared with the conventional method. This technique is a promising tool for preliminary thalassaemia screening in population prevention and control programmes. This study provides valuable references for designing specialised spectrometers.

Near-infrared (NIR) spectroscopy combined with the moving window partial least-squares (MWPLS) method and Savitzky–Golay (SG) smoothing was successfully applied for the rapid no-reagent determination of chemical oxygen demand (COD) in sugar refinery wastewater. An appropriate waveband with stability was selected through a rigorous modeling process. Among 141 samples, 60 were randomly selected as the validation set. The remaining 81 samples were divided into the calibration set (50 samples) and the prediction set (31 samples) for a total of 20 times with certain similarities. The results showed that the short-wave NIR region (780 nm to 1100 nm) can be used as the information waveband of COD in sugar refinery wastewater, and the optimal SG smoothing mode was 5th order derivative, 5th degree polynomial, and 7 smoothing points. The waveband selection was performed in the SG smoothing spectra of the short-wave NIR region by the MWPLS method. The optimized waveband was 808 nm to 964 nm, the validation root mean square error of prediction (V-SEP) and validation correlation coefficients of prediction (V-RP) were 25.0 mg L−1 and 0.966, respectively, which had good prediction precision and stability. The results provide a reliable NIR analysis model and valuable references for designing small, specialized NIR instruments.

Wavelength model optimisation is a fundamental core technology of near-infrared (NIR) spectroscopic analysis. An integrated optimisation method for wavelength selection of serum glucose analysis was proposed in the present study. A global parameter optimisation platform of Norris derivative filter (NDF) was established to achieve the most suitable spectral preprocessing. The equidistant combination-partial least squares (EC-PLS) combined with wavelength step-by-step phase-out-PLS (WSP-PLS) was proposed to achieve large-scale screening of discrete wavelengths model for serum glucose. As a result, the combination of eight wavelengths (1583, 1695, 1733, 1816, 1860, 2125, 2340, 2376 nm) was selected and a good prediction effect was obtained. It can provide a valuable reference for non-invasive blood glucose detection and designing dedicated spectrometers. The proposed WSP-PLS can optimise any wavelength model obtained by certain optimisation strategies, improve the prediction performance and reduce the wavelength model complexity. We believe it will have a wider application.