Determination of Branched-Amino Acid Content in Fermented Cordyceps sinensis Mycelium by Using FT-NIR Spectroscopy Technique

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Near-infrared spectroscopy based monitoring of all 20 amino acids in mammalian cell culture broth

The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL^-1), lactate (0-2.7 mg mL^-1), and trastuzumab (0-2.5 mg mL^-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm^-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (R_c² = 0.94-0.99) and prediction (R_p² = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (R_EV² = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview

Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.

Quality control method of sterols in fermented Cordyceps sinensis based on combined fingerprint and quantitative analysis of multicomponents by single marker

In this study, we established a new pattern for differentiating and comprehensively evaluating the quality of fermented Cordyceps sinensis based on high-performance liquid chromatography (HPLC) fingerprint analysis combined with similar analysis (SA), principal component analysis (PCA), hierarchical cluster analysis (HCA), and the quantitative analysis of multicomponents by single marker (QAMS). These methods indicated that fermented Cordyceps sinensis samples could be categorized into one class by PCA and HCA. The fingerprints of fermented Cordyceps sinensis were established, and four HPLC peaks were identified as ergosterol, daucosterol, stigmasterol, and β-sitosterol in Jinshuibao capsules and tablets (two products of fermented Cordyceps sinensis). Ergosterol was chosen as the internal reference substance, and the relative correction factors (RCFs) between ergosterol and the other three sterols were calculated using the QAMS method. Moreover, the accuracy of the QAMS method was confirmed by comparing the relative error between the results of the method used with those of an external standard method (ESM). No significant difference between the two methods was observed. The total sterols content in Jinshuibao products were calculated by the QAMS method, and the total sterols content of the two products were similar. This study showed that the method established herein was efficient and successful in the identification fermented Cordyceps sinensis and may further act to facilitate systematic quality control of fermented Cordyceps sinensis products.

HPLC determination of massoia lactone in fermented Cordyceps sinensis mycelium Cs-4 and its anticancer activity in vitro

The fermentation product of Cordyceps sinensis mycelium Cs-4 was commonly used as alternative substitutes of natural C. sinensis. Massoia lactone is the dominant component in the volatile oil of Cs-4 mycelium. In this research, we present a high performance liquid chromatography (HPLC) method for the quantitation of massoia lactone in Cs-4 mycelium. The high and stable contents of massoia lactone with values of 2.98-3.77 mg/g, indicated that massoia lactone could be considered as a marker for the quality assessment of this product. The results of MTT and CCK-8 assay showed that Cs-4 mycelium volatiles exhibited cytotoxicity against eight malignant tumor cells (IC₅₀  = 6.0-49.8 μg/ml) in comparison to the anticancer drug 5-fluorouracil (IC₅₀  = 17.0-425.3 μg/ml), and massoia lactone might be the chemical basis for the anticancer effects of Cs-4 mycelium. Compared to the commercial drugs paclitaxel and docetaxel (IC₅₀  = 253-1973 μg/ml), the Cs-4 mycelium volatiles and massoia lactone were discovered to possess inhibitory to taxol-resistant cell lines (IC₅₀  = 1.5-8.6 μg/ml). PRACTICAL APPLICATIONS: Considering that there is still a lack of marker components distinctive to Cs-4 mycelium, the HPLC method represents a useful tool for the quality evaluation of Cs-4 mycelium. Moreover, the volatile oil of Cs-4 mycelium and massoia lactone have prominent anticancer property in vitro. It gives a clue that Cs-4 mycelium, the volatile oil and massoia lactone could be potentially employed in the food and medical industries for its anticancer applications.

Effects of fermentation with Lactobacillus plantarum NCU137 on nutritional, sensory and stability properties of Coix (Coix lachryma-jobi L.) seed

This study aimed to investigate the effect of fermentation with Lactobacillus plantarum NCU137 on the nutritional, sensory and stability properties of Coix (Coix lachryma-jobi L.) seed. The nutritional compounds, including free amino acid, free fatty acid, soluble dietary fiber and organic acids of fermented coix seed were significantly (p < 0.05) increased than those of non-fermented coix seed. The fermented coix seed exhibiting a special flavor, due to the production of acids, the decreased level of aldehydes and ketones, and the increased level of alcohols in the volatile compounds, whereas the amount of hazardous substance 2-pentylfuran was reduced and natural antiseptic hexanoic acid was produced. The increased viscosity together with the larger particle size and the reduced absolute ζ potential contribute to the stability of the fermented coix seed paste system. Therefore, fermentation with L. plantarum NCU137 could improve the nutritional, sensory and stability properties of coix seed.

Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS-competitive adaptive reweighted sampling

A novel strategy of variable selection approach named dynamic backward interval partial least squares-competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS.

Applications of Non-destructive Technologies for Agricultural and Food Products Quality Inspection

The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation.

Key Variables Screening of Near-Infrared Models for Simultaneous Determination of Quality Parameters in Traditional Chinese Food “Fuzhu”

The traditional Chinese food Fuzhu is a dried soy protein-lipid film formed during the heating of soymilk. This study investigates whether a simple and accurate model can nondestructively determine the quality parameters of intact Fuzhu. The diffused reflectance spectra (1000–2499 nm) of intact Fuzhu were collected by a commercial near-infrared (NIR) spectrometer. Among various preprocessing methods, the derivative by wavelet transform method optimally enhanced the characteristic signals of Fuzhu spectra. Uninformative variable elimination based on Monte Carlo (MC-UVE), random frog (RF), and competitive adaptive reweighted sampling (CARS) were proposed to select key variables for partial least squares (PLS) calculation. The strong performance of the developed models is attributed to the high ratios of prediction to deviation values (3.32–3.51 for protein, 3.62–3.89 for lipid, and 4.27–4.55 for moisture). The prediction set was used to assess the performances of the best models of protein (CARS-PLS), lipid (RF-PLS), and moisture (CARS-PLS), which resulted in greater coefficients of determination of 0.958, 0.966, and 0.976, respectively, and lower root mean square errors of prediction of 0.656%, 0.442%, and 0.123%, respectively. Combined with chemometrics methods, the NIR technique is promising for simultaneous testing of quality parameters of intact Fuzhu.

Analysis of the High-Performance Liquid Chromatography Fingerprints and Quantitative Analysis of Multicomponents by Single Marker of Products of Fermented Cordyceps sinensis

Fermented Cordyceps sinensis, the succedaneum of Cordyceps sinensis which is extracted and separated from Cordyceps sinensis by artificial fermentation, is commonly used in eastern Asia in clinical treatments due to its health benefit. In this paper, a new strategy for differentiating and comprehensively evaluating the quality of products of fermented Cordyceps sinensis has been established, based on high-performance liquid chromatography (HPLC) fingerprint analysis combined with similar analysis (SA), hierarchical cluster analysis (HCA), and the quantitative analysis of multicomponents by single marker (QAMS). Ten common peaks were collected and analysed using SA, HCA, and QAMS. These methods indicated that 30 fermented Cordyceps sinensis samples could be categorized into two groups by HCA. Five peaks were identified as uracil, uridine, adenine, guanosine, and adenosine, and according to the results from the diode array detector, which can be used to confirm peak purity, the purities of these compounds were greater than 990. Adenosine was chosen as the internal reference substance. The relative correction factors (RCF) between adenosine and the other four nucleosides were calculated and investigated using the QAMS method. Meanwhile, the accuracy of the QAMS method was confirmed by comparing the results of that method with those of an external standard method with cosines of the angles between the groups. No significant difference between the two methods was observed. In conclusion, the method established herein was efficient, successful in identifying the products of fermented Cordyceps sinensis, and scientifically valid to be applicable in the systematic quality control of fermented Cordyceps sinensis products.

On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (R_p²) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.

Ultraviolet spectroscopy combined with ultra-fast liquid chromatography and multivariate statistical analysis for quality assessment of wild Wolfiporia extensa from different geographical origins

A quality assessment system comprised of a tandem technique of ultraviolet (UV) spectroscopy and ultra-fast liquid chromatography (UFLC) aided by multivariate analysis was presented for the determination of geographic origin of Wolfiporia extensa collected from five regions in Yunnan Province of China. Characteristic UV spectroscopic fingerprints of samples were determined based on its methanol extract. UFLC was applied for the determination of pachymic acid (a biomarker) presented in individual test samples. The spectrum data matrix and the content of pachymic acid were integrated and analyzed by partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The results showed that chemical properties of samples were clearly dominated by the epidermis and inner part as well as geographical origins. The relationships among samples obtained from these five regions have been also presented. Moreover, an interesting finding implied that geographical origins had much greater influence on the chemical properties of epidermis compared with that of the inner part. This study demonstrated that a rapid tool for accurate discrimination of W. extensa by UV spectroscopy and UFLC could be available for quality control of complicated medicinal mushrooms.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine.

Using FT-NIR spectroscopy technique to determine arginine content in fermented Cordyceps sinensis mycelium

This research investigated the feasibility of using Fourier transform near-infrared (FT-NIR) spectral technique for determining arginine content in fermented Cordyceps sinensis (C. sinensis) mycelium. Three different models were carried out to predict the arginine content. Wavenumber selection methods such as competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the most important wavenumbers and reduce the high dimensionality of the raw spectral data. Only a few wavenumbers were selected by CARS and CARS-SPA as the optimal wavenumbers, respectively. Among the prediction models, CARS-least squares-support vector machine (CARS-LS-SVM) model performed best with the highest values of the coefficient of determination of prediction (Rp(2)=0.8370) and residual predictive deviation (RPD=2.4741), the lowest value of root mean square error of prediction (RMSEP=0.0841). Moreover, the number of the input variables was forty-five, which only accounts for 2.04% of that of the full wavenumbers. The results showed that FT-NIR spectral technique has the potential to be an objective and non-destructive method to detect arginine content in fermented C. sinensis mycelium.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Application of near infrared spectroscopy for the rapid determination of epimedin A, B, C and icariin in Epimedium

More efficient and parsimonious models based on CARS-PLSR are obtained compared with the full-spectrum PLSR ones for the determination of epimedin A, B, C and icariin in Epimedium.

Color measurement of tea leaves at different drying periods using hyperspectral imaging technique

This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380–1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (r_p) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.

Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms

This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874–1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.