Study on quality analysis of different species of Coptidis rhizome based on fingerprint-effect relationship.

INTRODUCTION: The quality evaluation of Coptidis rhizome (CR) is attributed to the origin and processing method, and this strategy of ignoring the bioactive components usually leads to biased quality analysis, which is difficult to indicate the clinical efficacy. OBJECTIVES: In order to evaluate the quality level of different species of CR, we collected 20 batches of CR and investigated the fingerprint-effect relationship. METHODS: High-performance liquid chromatography (HPLC) fingerprints of CR were established, and the fingerprint-effect relationship was explored using cluster analysis, principal component analysis, Pearson correlation analysis, grey relation analysis, and partial least squares regression. RESULTS: We have identified a total of 10 common peaks (1-10) with similarity scores above 0.96. The study on the relationship between spectra and potency further showed that the contents of peaks 8, 9, and 10 are potential key components. And based on a previous study, a method of one measurement and multiple evaluations of CR was established to achieve the goal of simplifying the analytical process and reducing costs. CONCLUSION: Through a combination of fingerprint analysis, antioxidant activity evaluation, fingerprint-efficacy relationship analysis, and simultaneous quantification of multiple components, a CR quality control index and method have been selected and established, which can also provide a more comprehensive quality evaluation for traditional Chinese medicine.

Nondestructive discrimination of Potentilla anserina L. from different production areas based on near-infrared spectroscopy.

INTRODUCTION: The traditional Chinese medicine (TCM) Potentilla anserina L. can use both as food and medicine. At present, the market mainly depends on experience to identify the species and determine the production areas of P. anserina. To ensure the quality of P. anserina, it is essential to improve the level of quality control. OBJECTIVE: We aimed to establish a rapid and nondestructive discrimination model to identify P. anserina from different production areas by near-infrared spectroscopy. METHODS: The spectra of complete P. anserina medicinal materials and their powder of the same variety from four production areas were collected, and principal component analysis discriminant analysis and partial least squares discriminant analysis (PLS-DA) were conducted based on different pretreatment methods and band selection methods. Then, the spectra of complete medicinal materials were converted into the spectra of medicinal powder for nondestructive identification. RESULTS: The correct recognition rate (CRR) of the PLS-DA discriminant model was the best after spectral preprocessing using autoscaling and competitive adaptive reweighted sampling for band selection. The CRRs of the calibration set and validation set were 100%, the CRRs of the external test set were 95%, 90%, 82%, and 88%, respectively, and the CRRs of the transfer external test set were 84%, 80%, 82%, and 86%, respectively. CONCLUSION: We realized the nondestructive and effective identification of P. anserina from different origins and laid a foundation for the industrialization and upgrading of TCM.

Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case.

INTRODUCTION: Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price. OBJECTIVES: Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established. METHODS: We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed. RESULTS: The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model. CONCLUSION: The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.

Therapeutic effect and transcriptome-methylome characteristics of METTL3 inhibition in liver hepatocellular carcinoma.

Methyltransferase-like 3 (METTL3) is the key subunit of methyltransferase complex responsible for catalyzing N6-methyladenosine (m6A) modification on mRNA, which is the most prevalent post-transcriptional modification in eukaryotes. In this study, we utilized online databases to analyze the association between METTL3 expression and various aspects of tumorigenesis, including gene methylation, immunity, and prognosis. Our investigation revealed that METTL3 serves as a prognostic marker and therapeutic target for liver hepatocellular carcinoma (LIHC). Through experimental studies, we observed frequent upregulation of METTL3 in LIHC tumor tissue and cells. Subsequent inhibition of METTL3 using a novel small molecule inhibitor, STM2457, significantly impeded tumor growth in LIHC cell lines, spheroids, and xenograft tumor model. Further, transcriptome and m6A sequencing of xenograft bodies unveiled that inhibition of METTL3-m6A altered genes enriched in SMAD and MAPK signaling pathways that are critical for tumorigenesis. These findings suggest that targeting METTL3 represents a promising therapeutic strategy for LIHC.

Relaxin-Loaded Inhaled Porous Microspheres Inhibit Idiopathic Pulmonary Fibrosis and Improve Pulmonary Function Post-Bleomycin Challenges.

Idiopathic pulmonary fibrosis (IPF) causes worsening pulmonary function, and no effective treatment for the disease etiology is available now. Recombinant Human Relaxin-2 (RLX), a peptide agent with anti-remodeling and anti-fibrotic effects, is a promising biotherapeutic candidate for musculoskeletal fibrosis. However, due to its short circulating half-life, optimal efficacy requires continuous infusion or repeated injections. Here, we developed the porous microspheres loading RLX (RLX@PMs) and evaluated their therapeutic potential on IPF by aerosol inhalation. RLX@PMs have a large geometric diameter as RLX reservoirs for a long-term drug release, but smaller aerodynamic diameter due to their porous structures, which were beneficial for higher deposition in the deeper lungs. The results showed a prolonged release over 24 days, and the released drug maintained its peptide structure and activity. RLX@PMs protected mice from excessive collagen deposition, architectural distortion, and decreased compliance after a single inhalation administration in the bleomycin-induced pulmonary fibrosis model. Moreover, RLX@PMs showed better safety than frequent gavage administration of pirfenidone. We also found RLX-ameliorated human myofibroblast-induced collagen gel contraction and suppressed macrophage polarization to the M2 type, which may be the reason for reversing fibrosis. Hence, RLX@PMs represent a novel strategy for the treatment of IPF and suggest clinical translational potential.

Nondestructive rapid identification of wild Cordyceps sinensis with portable instrument.

INTRODUCTION: Cordyceps sinensis (CS) is a precious medicinal fungus. Wild CS (WCS) and artificial CS (ACS) are destroyed for their identification using traditional methods, which are time consuming and labor-intensive. Therefore, it is crucial to establish a nondestructive identification method to rapidly screen WCS. OBJECTIVE: The aim of this study was to provide technical support for rapid screening of CS and evaluation of its quality. The applicability of the model was improved through model transfer. METHODS: In this study, continuous wavelet transform was used to analyze the differences in moisture content and active components between WCS and ACS from the perspective of characteristic molecular groups. A portable instrument and a laboratory benchtop instrument were used to determine CS spectra. Partial least squares discrimination analysis was conducted for the identification of WCS and ACS while preserving the original shape of CS. Moreover, improved principal component analysis was utilized to transfer the model between the two types of near-infrared spectroscopy (NIRS) instruments. RESULTS: The results demonstrated that three peaks, at 1443, 1941, and 2183 nm, were characteristic absorption peaks. The model based on NIRS could initially provide rapid differentiation between WCS and ACS. At the same time, the accuracy of the external test set was further improved to over 95% through forward transfer. CONCLUSION: Therefore, this method could be used for rapid screening of WCS and provides technical support for the nondestructive identification of CS and initial assessment of CS quality.

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning.

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA-A and LMWHA-E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA-A and LMWHA-E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA-A and LMWHA-E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares-discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)-SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA-A and LMWHA-E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

Improved Principal Component Analysis (IPCA): A Novel Method for Quantitative Calibration Transfer between Different Near-Infrared Spectrometers.

Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. Recently, many new algorithms have been proposed for the model transfer of spectra collected by the same types of instruments under different situations. However, in a practical scenario, we need to deal with model transfer between different types of instruments. To expand model applicability, we must develop a method that could transfer spectra acquired from different types of NIR spectrometers with different wavenumbers or absorbance. Therefore, in our study, we propose a new methodology based on improved principal component analysis (IPCA) for calibration transfer between different types of spectrometers. We adopted three datasets for method evaluation, including public pharmaceutical tablets (dataset 1), corn data (dataset 2), and the spectra of eight batches of samples acquired from the plasma ethanol precipitation process collected by FT-NIR and MicroNIR spectrometers (dataset 3). In the calibration transfer for public datasets, IPCA displayed comparable results with the classical calibration transfer method using piecewise direct standardization (PDS), indicating its obvious ability to transfer spectra collected from the same types of instruments. However, in the calibration transfer for dataset 3, our proposed IPCA method achieved a successful bi-transfer between the spectra acquired from the benchtop and micro-instruments with/without wavelength region selection. Furthermore, our proposed method enabled improvements in prediction ability rather than the degradation of the models built with original micro spectra. Therefore, our proposed method has no limitations on the spectrum for model transfer between different types of NIR instruments, thus allowing a wide application range, which could provide a supporting technology for the practical application of NIR spectroscopy.

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration.

Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. "Data binning" was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. "Normalized mutual information" was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

Tunable Chirality of Self-Assembled Dipeptides Mediated by Bipyridine Derivative.

Supramolecular peptide assemblies have been widely used for the development of biomedical, catalytical, and optical materials with chiral nanostructures in view of the intrinsic chirality of peptides. However, the assembly pathway and chiral transformation behavior of various peptides remain largely elusive especially for the transient assemblies under out-of-equilibrium conditions. Herein, the N-fluorenylmethoxycarbonyl-protected phenylalanine-tyrosine dipeptide (Fmoc-FY) was used as a peptide assembly platform, which showed that the assembly proceeds multistep evolution. The original spheres caused by liquid-liquid phase separation (LLPS) can nucleate and elongate into the formation of right-handed helices which were metastable and easily converted into microribbons. Interestingly, a bipyridine derivative can be introduced to effectively control the assembly pathway and induce the formation of thermodynamically stable right-handed or left-handed helices at different stoichiometric ratios. In addition, the chiral assembly can also be regulated by ultrasound or enzyme catalysis. This minimalistic system not only broadens the nucleation-elongation mechanisms of protein aggregates but also promotes the controllable design and development of chiral biomaterials.

Implementation of Dynamic and Static Moisture Control in Fluidized Bed Granulation.

The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.

Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review.

Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.

Quality evaluation of traditional Chinese medicines based on fingerprinting.

The usage of traditional Chinese medicines has expanded globally, but the data about authentication, efficacy, and safety is far from sufficient to meet the criteria supporting their use worldwide due to complexity in the composition. Fingerprinting describes integral characterization and reflects interactive aspects of complex components; therefore, it can offer the possibility of evaluating quality of traditional Chinese medicines following the overall principle. Chemometric techniques introduce multivariate analytical methods into fingerprinting to obtain more information that is useful, which is consistent with the holistic thought and plays an important role in research on the substantial basis. In this review, we will start with three aspects to expound the quality evaluation of traditional Chinese medicines based on fingerprints. The analytical techniques used in developing fingerprints including chromatographic methods, spectroscopic methods, and capillary electrophoresis are introduced. Strategies for fingerprints analysis usually based on chemometric methods including unsupervised and supervised pattern recognition are described. Applications of fingerprints for multi-component quantification, quality control, screening of bioactive components, and fingerprint-efficacy relationship study are also outlined. Finally, we propose challenges and future perspectives of fingerprints in quality evaluation to promote the development of modernization and internationalization of traditional Chinese medicines.

Preparation of Low Molecular Weight Chondroitin Sulfates, Screening of a High Anti-Complement Capacity of Low Molecular Weight Chondroitin Sulfate and Its Biological Activity Studies in Attenuating Osteoarthritis.

Chondroitin sulfate (CS) plays important roles in the complement system. However, the CS structure is complicated due to different sources and the number and positions of sulfate groups. The objective of this study was to prepare different low molecular weight chondroitin sulfates (LMWCSs) and to investigate the biological activity in anti-complement capacity. A series of LMWCSs was prepared from different sources and characterized by ultraviolet-visible (UV) spectroscopy, high-performance liquid chromatography (HPLC), size exclusion chromatography-multiangle laser light scattering (SEC-MALLS) and nuclear magnetic resonance (NMR) spectroscopy. Hemolytic, anti-complement 3 deposition capacity and cell viability assays were carried out to investigate the biological activities in vitro. The results showed that LMWCS prepared from shark cartilage with the oxidative degradation method (LMWCS-S-O) had the best anti-complement capacity. LMWCS-S-O could inhibit the alternative pathway of the complement system and protect chondrocytes from cell death. The attenuating effect of LMWCS-S-O on Osteoarthritis (OA) was investigated by destabilization of the medial meniscus (DMM) model in vivo. Functional wind-up, histological and C5b-9 analyses were used to evaluate the treatment effect on the OA model. In vivo results showed that LMWCS-S-O could attenuate OA. LMWCS-S-O with a high content of ΔDi-2,6diS and ΔDi-6S could be used for attenuating OA through regulating the complement system.

[Rapid monitoring five components of ethanol precipitation process of Shenzhiling oral solution using near infrared spectroscopy].

To develop a method for the rapid monitoring of five components during the alcohol precipitation process of Shenzhiling oral solution using near infrared spectroscopy(NIRS).The contents of five components detemined by high performance liquid chromatography(HPLC) were used as the reference values, and the NIRS based partial least square regression(PLSR) models were used to monitor the concentrations of paeoniflorin, albiflorin, liquiritin, cinnamic acid and glycyrrhizic acid during the alcohol precipitation process of Shenzhiling oral solution, which were optimized and verified through comparing of different spectral pre-processing and variables selection methods. Determination coefficients(Rcal2 and Rpred2), root mean squares error of prediction (RMSEP), root mean squares error of calibration(RMSEC) and ratiao of performance to deviation(RPD) were applied to evaluate the performance of the models, and the corresponding values were 0.993 3 and 0.997 6, 0.084 9 g•L⁻¹, 0.073 3 g•L⁻¹ and 14.7 for paeoniforin; 0.991 4, 0.992 7, 0.028 1 g•L⁻¹, 0.030 5 g•L⁻¹ and 10.2 for albiforin; 0.955 3, 0.976 1, 0.012 0 g•L⁻¹, 0.012 3 g•L⁻¹ and 5.1 for liquiritin; 0.958 8, 0.990 3, 0.003 89 g•L⁻¹, 0.002 89 g•L⁻¹ and 7.1 for cinnamic acid; 0.982 0, 0.986 3, 0.053 8 g•L⁻¹, 0.059 0 g•L⁻¹, 7.2 for glycyrrhizic acid, respectively. The results indicated that the presented approach was effectively for the quantitative monitoring of the alcohol precipitation process of Shenzhiling oral solution.

Fingerprint analysis, multi-component quantitation, and antioxidant activity for the quality evaluation of Salvia miltiorrhiza var. alba by high-performance liquid chromatography and chemometrics.

Salvia miltiorrhiza Bge. var. alba C.Y. Wu and H.W. Li has wide prospects in clinical practice. A useful comprehensive method was developed for the quality evaluation of S. miltiorrhiza var. alba by three quantitative parameters: high-performance liquid chromatography fingerprint, ten-component contents, and antioxidant activity. The established method was validated for linearity, precision, repeatability, stability, and recovery. Principal components analysis and hierarchical clustering analysis were both used to evaluate the quality of the samples from different origins. The results showed that there were category discrepancies in quality of S. miltiorrhiza var. alba samples according to the three quantitative parameters. Multivariate linear regression was adopted to explore the relationship between components and antioxidant activity. Three constituents, namely, danshensu, rosmarinic acid, and salvianolic acid B, significantly correlated with antioxidant activity, and were successfully elucidated by the optimized multivariate linear regression model. The combined use of high-performance liquid chromatography fingerprint analysis, simultaneous multicomponent quantitative analysis, and antioxidant activity for the quality evaluation of S. miltiorrhiza var. alba is a reliable, comprehensive, and promising approach, which might provide a valuable reference for other herbal products in general to improve their quality control.

[Identification of Metoprolol Tartrate Tablets from Different Manufacturers by Different Near Infrared Spectrometers].

To rapidly identify Metoprolol Tartrate tablets from different manufacturers, a qualitative analysis model can be established by near infrared spectroscopy. Firstly, AntarisII Fourier-transform near infrared (FT-NIR) spectroscopy and Micro NIR1700 Spectrometer were used to collect spectral data of 66 batches of samples which come from four different manufacturers, then 44 samples of calibration set and 22 samples of validation set were acquired by random sampling. In order to build up a PLS-DA model, the first derivative with Savitzky-Golay 15 points smoothing (1d+SG15) and standard normal vitiate transformation (SNV) was selected as the pretreatment method and according to the variation between different samples and the characteristic absorption band, 6 468-7 104 cm(-1) and 6 468-7 156 cm(-1) were chosen as the modeling spectra region. The confusion matrix indicated that Metoprolol Tartrate tablets could be rapidly and effectively identified by two analytical models, which were established using the spectral data collected from two instruments. For these two models, both of the sensitivity and specificity were 100%. This study confirmed that it is feasible to carry out the manufacturer identification of Metoprolol Tartrate tablets by near infrared spectroscopy. Besides, the use of Micro NIR1700 Spectrometer, which is the minimum and portable near infrared spectrometer, provides valuable insights for fast on-site drug screening.

Absolutely nondestructive discrimination of Huoshan Dendrobium nobile species with miniature near-infrared (NIR) spectrometer engine.

As one very precious traditional Chinese medicine (TCM), Huoshan Dendrobium has not only high price, but also significant pharmaceutical efficacy. However, different species of Huoshan Dendrobium exhibit considerable difference in pharmaceutical efficacy, so rapid and absolutely non-destructive discrimination of Huoshan Dendrobium nobile according to different species is crucial to quality control and pharmaceutical effect. In this study, as one type of miniature near-infrared (NIR) spectrometer, MicroNIR 1700 was used for absolutely nondestructive determination of NIR spectra of 90 batches of Dendrobium from five species of differ- ent commodity grades. The samples were intact and not smashed. Soft independent modeling of class analogy (SIMCA) pattern recognition based on principal component analysis (PCA) was used to classify and recognize different species of Dendrobium samples. The results indicated that the SIMCA qualitative models established with pretreatment method of standard normal variate transformation (SNV) in the spectra range selected by Qs method had 100% recognition rates and 100% rejection rates. This study demonstrated that a rapid and absolutely non-destructive analytical technique based on MicroNIR 1700 spectrometer was developed for successful discrimination of five different species of Huoshan Dendrobium with acceptable accuracy.

Three Stages of Dynamic Assembly Process of Dipeptide-Based Supramolecular Gel Revealed by In Situ Infrared Spectroscopy.

The exploration of short peptide-based assembly is vital for understanding protein-misfolding-associated diseases and seeking strategies to attenuate aggregate formation. While, the molecular mechanism of their structural evolution remains poorly studied in view of the dynamic and unpredictable assembly process. Herein, infrared (IR) spectroscopy, which serves as an in situ and real-time analytical technique, was intelligently employed to investigate the mechanism of phase transition and aggregate formation during the dynamic assembly process of diphenylalanine. Combined with other spectroscopy and electron microscopy technologies, three stages of gel formation and the main driving forces in different stages were revealed. A variety of stoichiometric methods such as continuous wavelet transform, principal component analysis, and two-dimensional correlation spectroscopy techniques were conducted to analyze the original time-dependent IR spectra to obtain detailed information on the changes in the amide bands and hydration layer. The microenvironment of hydrogen bonding among amide bands was significantly changed with the addition of pyridine derivatives, resulting in great differences in the properties of co-assembled gels. This work not only provides a universal analytical way to reveal the dynamic assembly process of dipeptide-based supramolecular gel but also expands their applications in supramolecular regulation and high-throughput screens in situ.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy.

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.