Rapid quality evaluation of moutan cortex (Paeonia suffruticosa Andrews) by near-infrared spectroscopy and bionic swarm intelligent optimization algorithm.

Rapid multi-indicator quality evaluation of Eucommia ulmoides by near-infrared spectroscopy combined with chemometrics

Rapid quality evaluation of Plantaginis Semen by near infrared spectroscopy combined with chemometrics

Moutan Cortex(MC) residues produced after the extraction of MC can be re-extracted for active components and used to produce organic fertilizer and animal feed. However, they are currently disposed as domestic waste, which pollutes the environment. This study analyzed the chemical composition of the medicinal material, residues, and residue compost of MC by UPLC-UV-Q-TOF-MS. Furthermore, the nutrient composition of MC residues and the residue compost was analyzed. The results showed that:(1)MC residues had lower content of chemicals than the medicinal material, and content of paeonol, gallic acid, and galloylglucose in MC residues were about 1/3 of that in the medicinal material. The content of chemicals were further reduced after residue composting, and the quantitative compounds were all below the limits of detection.(2)Compared with MC residues, the residue compost showed the total nitrogen, total phosphorus, total potassium, and organic matter content increasing by 122.67%, 31.32%, 120.39%, and 32.06%, respectively. Therefore, we concluded that the MC residues can be used to re-extract active compounds such as paeonol, gallic acid, and galloylglucose. The MC residue compost is a high-quality organic fertilizer containing minimal content of chemicals and can be widely used in the cultivation of Chinese medicinal herbs.

NIRS to assess chemical composition of sheep and goat cheese

The present study determines the potential antioxidants in Moutan Cortex (MC) and predicts its targets of anti-oxidative activities. The quantitative analysis and the free radical scavenging assays were conducted to detect the main components in MC and assess its anti-oxidant activities. The grey relational analysis and the network pharmacology approach were employed to predict its key components and targets of anti-oxidant activities. Six main constitutes in MCs were quantified by high performance liquid chromatography (HPLC) and its anti-oxidant activities were evaluated by DPPH and ABTS free radical scavenging methods. Then grey relational analysis was employed to predict the key components acting on anti-oxidative activity based on the chem-bio results. The predicted components and its mechanisms on anti-oxidation were uncovered by network pharmacology approach and cell test, respectively. The content of paeonol and paeoniflorin accounts for more than 80% the whole content of detected components. However, the two main ingredients showed a great variety among MCs. The antioxidant capacities of MCs also showed a great discrepancy based on DPPH and ABTS methods. The key components acting on anti-oxidation were identified to be paeonol, gallic acid and benzoylpaeoniflorin, and their potential therapeutic targets were predicted and verified, respectively. The present results reveal that MC has a significant antioxidant activity and the compounds of paeonol, gallic acid and benzoylpaeoniflorin could be considered as the promising antioxidant candidates with the property of suppressing oxidative stress and apoptosis.

Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham

Relationship between sensory and NIR spectroscopy in consumer preference of table grape (cv Italia)

The beneficial health effects of traditional Chinese medicines are often attributed to their potent antioxidant activities, usually established in vitro. However, these wet chemical methods for determining antioxidant activities are time-consuming, laborious, and expensive. This study was conducted to establish a rapid determination of antioxidant activity of Radix Scutellariae using near-infrared (NIR) and mid-infrared (MIR) spectroscopy. Antioxidant capabilities were evaluated using 2,2-diphenyl-1-picrylhydrazyl hydrazyl (DPPH) and oxygen radical absorbance capacity (ORAC) assays. The total flavonoid contents (TFCs) of Radix Scutellariae were measured by the aluminium chloride colorimetric method. The same sample was then scanned using NIR and MIR spectroscopy. Chemometrics analysis using partial least-squares (PLS) regression was performed to establish the models for predicting the antioxidant activities of Radix Scutellariae. A better predictive performance was achieved using PLS models based on NIR data. The determination coefficient (R(2)) and the residual predictive deviation (RPD) for the validation set were 0.9298 and 2.84 for DPPH, and 0.9436 and 2.66 for TFCs, respectively. MIR-PLS algorithms gave a slightly lower reliability (R(2) = 0.9090 and 0.9374, RPD = 2.01 and 2.42, for DPPH and TFC, respectively). Very comparable results for ORAC were obtained with the two methods. The developed spectroscopic method can be successfully applied in high-throughput screening of the antioxidant capability of Radix Scutellariae samples. It can also be a viable and advantageous alternative to laborious chemical procedures.

The effect of two drying methods (oven and freeze drying) and the addition of maltodextrin to Kakadu plum puree samples (KP) (Terminalia ferdianandiana) were evaluated using mid (MIR) and near-infrared (NIR) spectroscopy. Dry powder samples were obtained using the oven and freeze-drying methods and seven levels of maltodextrin. Training (n = 32) and validation (n = 28) sets were developed for the prediction of moisture (M %), water activity (aw %), hydroxymethylfurfural (HMF) and vitamin C (VITC mg/100 g DM) based on NIR and MIR spectroscopy. Results from this study demonstrated the ability of spectroscopy combined with partial least squares (PLS) regression to monitor these parameters during drying. The standard error in cross validation (SECV) and the residual predictive deviation (RPD) values obtained were of 0.71% (RPD = 4.1) and 0.47% (RPD = 6.1) for M, 0.06% (RPD = 4.4) and 0.02% (RPD = 8.2) for aw, 0.73 (RPD = 3.3) and 0.72 (RPD = 3.3) for HMF, 465.7 mg 100 g DM (RPD = 3.0) and 289.3 mg 100 g DM (RPD = 4.8) for VITC, using MIR and NIR, respectively. The results from this study showed that MIR and NIR spectroscopies are capable of both measuring and monitoring the effect of drying and the addition of maltodextrin as a carrier to KP puree samples.

Phosphorus is among the main limiting nutrients for plant growth and productivity in both agricultural and natural ecosystems in the tropics, which are characterized by weathered soil. Soil bioavailable P measurement is necessary to predict the potential growth of plant biomass in these ecosystems. Visible and near-infrared reflectance spectroscopy (Vis-NIRS) is widely used to predict soil chemical and biological parameters as an alternative to time-consuming conventional laboratory analyses. However, quantitative spectroscopic prediction of soil P remains a challenge owing to the difficulty of direct detection of orthophosphate. This study tested the performance of Vis-NIRS with partial least square regression to predict oxalate-extractable P (Pox) content, representing available P for plants in natural (forest and non-forest including fallows and degraded land) and cultivated (upland and flooded rice fields) soils in Madagascar. Model predictive accuracy was assessed based on the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). The results demonstrated successful Pox prediction accuracy in natural (n = 74, R² = 0.90, RMSECV = 2.39, and RPD = 3.22), and cultivated systems (n = 142, R² = 0.90, RMSECV = 48.57, and RPD = 3.15) and moderate usefulness at the regional scale incorporating both system types (R² = 0.70, RMSECV = 71.87 and RPD = 1.81). These results were also confirmed with modified bootstrap procedures (N = 10,000 times) using selected wavebands on iterative stepwise elimination–partial least square (ISE–PLS) models. The wavebands relevant to soil organic matter content and Fe content were identified as important components for the prediction of soil Pox. This predictive accuracy for the cultivated system was related to the variability of some samples with high Pox values. However, the use of “pseudo-independent” validation can overestimate the prediction accuracy when applied at site scale suggesting the use of larger and dispersed geographical cover sample sets to build a robust model. Our study offers new opportunities for P quantification in a wide range of ecosystems in the tropics.

Near-infrared spectroscopy (NIRS) is applied in pharmaceutical industry for monitoring drug content during tablet manufacturing process. NIRS method, once developed and validated, is used over years and it is of critical importance to insure method robustness towards formulation, process, instrumental, acquisition and environmental factors. Design of Experiments (DoE) methodology was proposed in this work for systematic study of the effect of compression pressure, pre-compression pressure and tableting speed on Average Euclidean Distance (AED) which reflects NIR spectral features of the studied caffeine tablets, and Root Mean Squared Error of Prediction (RMSEP) as a key performance indicator of the developed NIRS calibration model for caffeine content prediction. Study was performed in diffuse reflectance (DR) and diffuse transmittance (DT) measurement mode. Tableting factors shown to have significant influence on the studied responses have been considered in the development of the robust calibration models in DR and DT mode, using Global Calibration Model (GCM) approach. Three studied factors have shown to be significant in DR mode whereas, compression pressure and tableting speed have shown significant effect on the studied responses in DT mode. Developed robust method in DT mode have shown superior performances compared to DR mode, exhibiting total error (RMSEP) of 1.21 % calculated on the independent test set. DoE setup, with the selection of factors and responses adopted in this study was not reported elsewhere. Simultaneous NIRS quantification of two APIs in powders and tablets requires several challenges to be overcome. Overlapping absorption peaks of formulation components result in method specificity problem. Strategy for selecting the samples used for developing the prediction models is needed. Robustness of the method towards formulation factors needs to be assessed due to complex formulation. Fast and simple method for simultaneous quantification of Hydrochlorothiazide (HTZ) and Metoprolol Tartrate (MTP) in powders and tablets was proposed in work. Simulation of industrial scale tablet machine using tablet press replicator - Presster® was proposed as fast and cost-effective alternative for design and manufacture of tablet sets needed for NIRS calibration model development. Balance Reference Method (BRM) was proposed as an alternative to HPLC and UV-spectroscopy which are traditionally used as reference methods in NIRS model development. The proposed experimental setup was suggested for the feasibility study stage of the method development. The two model drugs were simultaneously quantified using NIRS exhibiting RMSEP of 1.69 and 1.31 mg in HTZ powder and tablet samples respectively, while MTP powder and tablet samples were predicted with RMSEP of 3.15 and 3.00 mg respectively. NIRS analysis of Metoprolol Tartrate and Hydrochlorothiazide in powders and tablets was not yet reported in the literature. The compressibility and compatibility of a powder formulation is conventionally determined by compaction followed by destructive tensile strength and relative density measurement of the final compact. In this study, a non-destructive near-infrared spectroscopic (NIRS) was evaluated for the determination of powder compressibility and compactibility. Twelve different formulations were investigated with 2 batches produced per formulation. Relative density and tensile strength were measured using a traditional, destructive method on one tablet batch and subsequently by a developed non-destructive chemometric NIRS method on the second batch of the particular formulation. The outcomes of the two approaches were compared to validate the developed method. All data sets were fitted to the three established mathematical equations to calculate equation factors, which represent a formulation compressibility and compactibility. The study focus was set on the equation factor comparison between the traditional and the newly designed method. The results have shown a high degree similarity between the outcomes of the two methods. A discrepancy between the two methods was observed for the outcomes of the equation factors after fitting to Leuenberger equation. The approach using NIRS is suggested as a promising tool for monitoring tablet manufacturing process.

Cistanche tubulosa, as a homology of medicine and food, not only has a unique medicinal value but also is widely used in healthcare products. Polysaccharide is one of its important quality indicators. In this study, an analytical model based on near-infrared (NIR) spectroscopy combined with machine learning was established to predict the polysaccharide content of C. tubulosa. The polysaccharide content in the samples determined by the phenol-sulfuric acid method was used as a reference value, and machine learning was applied to relate the spectral information to the reference value. Dividing the samples into a calibration set and a prediction set using the Kennard-Stone algorithm. The model was optimized by various preprocessing methods, including Savitzky-Golay (SG), standard normal variate (SNV), multiple scattering correction (MSC), first-order derivative (FD), second-order derivative (SD), and combinations of them. Variable selection was performed through the successive projections algorithm (SPA) and stability competitive adaptive reweighted sampling (sCARS). Four machine learning models were used to build quantitative models, including the random forest (RF), partial least-squares (PLS), principal component regression (PCR), and support vector machine (SVM). The evaluation indexes of the model were the coefficient of determination (R2), root-mean-square error (RMSE), and residual prediction deviation (RPD). RF performs best among the four machine learning models. R2c （calibration set coefficient of determination） and RMSEC （root mean square error of the calibration set）, %, were 0.9763. and 0.3527 for calibration, respectively. R2p （prediction set coefficient of determination）, RMSEP （root mean square error of the prediction set）, %, and RPD were 0.9230, 0.5130, and 3.33 for prediction, respectively. The results indicate that NIR combined with the RF is an effective method applied to the quality evaluation of the polysaccharides of C. tubulosa. Four quantitative models were developed to predict the polysaccharide content in C. tubulosa, and good results were obtained. The characteristic variables were basically determined by the sCARS algorithm, and the corresponding characteristicgroups were analyzed.

Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan agricultural regions using NIR spectroscopy

ProTformer: Transformer-based model for superior prediction of protein content in lablab bean (Lablab purpureus L.) using Near-Infrared Reflectance spectroscopy

Quantitative (1)H-NMR ((1)H-qNMR) was applied to the determination of paeonol concentration in Moutan cortex, Hachimijiogan, and Keishibukuryogan. Paeonol is a major component of Moutan cortex, and its purity was calculated from the ratio of the intensity of the paeonol H-3' signal at δ 6.41ppm in methanol-d 4 or 6.40ppm in methanol-d 4+TFA-d to that of a hexamethyldisilane (HMD) signal at 0ppm. The concentration of HMD was corrected with SI traceability by using potassium hydrogen phthalate of certified reference material grade. As a result, the paeonol content in two lots of Moutan cortex as determined by (1)H-qNMR was found to be 1.59% and 1.62%, respectively, while the paeonol content in Hachimijiogan and Keishibukuryogan was 0.15% and 0.22%, respectively. The present study demonstrated that the (1)H-NMR method is useful for the quantitative analysis of crude drugs and Kampo formulas.