Root Mean Square Prediction Research Articles

A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium‐Ion Batteries

This paper proposes a method for estimating the state of health (SOH) of lithium‐ion batteries (LIBs) using a combination of vision transformer (VIT) and gated recurrent unit (GRU) networks. The new scheme adopts a VIT to extract features from the battery measured data and incorporates a GRU network to mitigate the limitations of the VIT caused by positional encoding. The resulting VIT‐GRU network is designed to comprehensively capture information relevant to the battery SOH. Simulation experiments on the NASA dataset illustrate the notable results achieved by the VIT‐GRU, with prediction root mean square error (RMSE) and mean absolute error (MAE) up to 0.54% and 0.38%, respectively, demonstrating the exceptional performance of the VIT‐GRU network in SOH estimation. Compared to other complex deep learning (DL) methods, the VIT‐GRU significantly outperforms them, according to the RMSE and MAE of the predicted values.

Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Abstract The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

Prediction of mine water quality by the Seq2Seq model based on attention mechanism

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and Sequence to Sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction.

A Spectral Detection Method Based on Integrated and Partition Modeling for Trace Copper in High-Concentration Zinc Solution.

In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid.

Estimation of Time-Since-Deposition of bloodstains on different surfaces using ATR-FTIR Spectroscopy and Chemometrics.

Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800cm- 1 to 3500cm- 1.

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10−3, 3.03 × 10−3, and 30.66 × 10−3, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction.

A critique and examination of the polysocial risk score approach: predicting cognition in the Health and Retirement Study.

Polysocial risk scores were recently proposed as a strategy for improving the clinical relevance of knowledge about social determinants of health. Our objective in this study was to assess whether the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13 773 individuals aged ≥50 years at baseline from the 2006-2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort study. Four linear mixed models were compared: 2 simple models including a priori-selected covariates and 2 polysocial risk score models which used least absolute shrinkage and selection operator (LASSO) regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R2 and root mean-squared prediction error (RMSPE) using training/test split validation and cross-validation. For predicting cognition, the simple model including age, race, sex, and education had an R2 value of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R2=0.35 and RMSPE=0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, sex, and education had an area under the receiver operating characteristic curve (AUROC) of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC=0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social "risk factors."

基于可见/近红外光谱的鲜枣可溶性固形物在线检测

Soluble solid content (SSC) is one of the important evaluation indexes of the internal quality and taste of fresh jujube. In order to realize the online nondestructive detection of SSC of fresh jujube, this paper took Huping jujube as the research object, adopted self-constructed nondestructive online testing system to collect the spectral information of jujubes (350~2500 nm), and studied the influence of the rotational speed of 4 r/min on the online prediction model of SSC of jujube. Kennard-Stone (KS) algorithm was used to divide the sample into correction set and prediction set. Six commonly used preprocessing methods such as SG smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), orthogonal signal correction (OSC), first derivative (FD), and second derivative (SD) were applied to the spectral data, and the regression coefficient (RC) algorithm and the successive projections algorithm (SPA) were utilized to select informative wavelengths, and a quantitative prediction model for the SSC of Huping jujube was established using partial least squares regression (PLSR). The results indicate that the PLSR prediction model established by preprocessing the original spectrum with OSC and combining it with RC algorithm to select characteristic wavelengths was optimal. Therefore, when predicting the SSC of Huping jujube, the optimal model was OSC-RC-PLSR, and the correlation coefficients of the correction set and prediction set were 0.846 and 0.782, respectively, and the corrected root mean square error (RMSEC) and predicted root mean square error (RMSEP) were 1.962 and 2.247, respectively. The results show that non-destructive detection of soluble solid content of jujube can be achieved by combining visible-near-infrared spectroscopy and appropriate regression model, which provides an innovative way for online sorting and identifying fresh jujube.

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Significant Wave Height (SWH) is a crucial parameter in ocean wave dynamics, impacting coastal safety, maritime transportation, and meteorological research. Building upon the TimesNet neural network, a recent advancement in the realm of time series prediction in deep learning, this study proposes an integrated approach combining Empirical Mode Decomposition (EMD) with TimesNet, introducing the EMD-TimesNet model for SWH forecasting. The TimesNet model’s multidimensional spatial mapping guarantees effective historical information extraction, while the EMD approach makes it easier to decompose subsequence characteristics inside the original SWH data. The predicted Root Mean Square Error (RMSE) and Correlation Coefficient (CC) values of the EMD-TimesNet model are 0.0494 m and 0.9936; 0.0982 m and 0.9747; and 0.1573 m and 0.9352 at 1 h, 3 h, and 6 h, respectively. The results indicate that the EMD-TimesNet model outperforms existing models, including the TimesNet, Autoformer, Transformer, and CNN-BiLSTM-Attention models, both in terms of overall evaluation metrics and prediction performance for diverse sea states. This integrated model represents a promising advancement in enhancing the accuracy of SWH predictions.

A portable dry film FTIR instrument for industrial food and bioprocess applications.

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

A Data‐Driven‐Aided Thermoelectric Equivalent Circuit Model for Accurate Temperature Prediction of Lithium‐Ion Batteries

Accurate temperature estimation models for lithium (Li)‐ion batteries are critical for timely identification of and response to thermal runaway effects to ensure battery safety. In this paper, a hybrid data‐driven approach incorporating thermoelectric equivalent model (TEM) is proposed to predict the temperature of Li‐ion batteries under different state of health (SOH) based on measured data. The proposed TEM model consists of an electrical equivalent circuit model (EECM) and a thermal equivalent circuit modeling (TECM). The electrical model is a second‐order RC equivalent circuit model, and the thermal model is a first‐order thermal model, which interacts with parameters such as state of charge (SOC) and internal resistance to improve the accuracy of the model. In order to solve the problem that the model part is susceptible to measurement errors, a data‐driven model using Kalman filter (KF) combined bidirectional gated recursive unit (BiGRU) and Transformer is proposed to ensure high accuracy in predicting the temperature. The output of the TEM is used as the input to the data‐driven part to obtain the implied relationship between the temperature and parameters. The experimental results confirm the high accuracy of the hybrid model in estimating the battery temperature. The maximum temperature prediction error of the Li‐ion battery was 0.3423°C with a predicted root mean square error (RMSE) of 0.1266 under different SOH conditions.

Prediction models of texture attributes for radiofrequency assisted hot air drying of jujube slices based on morphological parameters

The effects of temperature and radiofrequency (RF) treatment on the texture attributes and morphological parameters of jujube slices were investigated using radiofrequency-assisted hot air drying (RFHAD). Drying temperature and RF treatment time have a significant influence on texture attributes such as chewiness and cohesiveness and morphological parameters such as area, roundness, and perimeter of jujube slices. The correlation between texture attributes and morphological parameters showed that area, roundness and perimeter were highly correlated with cohesiveness (P < 0.01), and chewiness was significantly correlated with circumference (P < 0.05). The regression model was estimated by using back propagation neural network (BPNN), partial least squares regression (PLSR), and multi-linear regression (MLR). Then, the texture attributes of jujube slices were predicted according to the changes of morphological parameters. BPNN predicted root mean square error (RMSE) of 1.1116 and 0.0091 for chewiness and cohesiveness, respectively, and coefficient of determination (R2) of 0.9943 and 0.9893 for chewiness and cohesiveness, respectively. In addition, BPNN obtained the highest R2 and lowest RMSE compared with PLSR and MLR. Therefore, BPNN can be used to explore the change mechanism of texture attributes of jujube slices from a macro point of view and provide a new idea for the drying technology of jujube slices.

Quantitative Analysis of Mixtures Based on Portable Spatial Heterodyne Raman Spectrometer

Spatial heterodyne Raman spectroscopy has been widely applied in various fields due to its non-contact, nondestructive, fast, high stability, and high spectral resolution characteristics. This article integrated spatial heterodyne Raman spectroscopy with chemometric methodologies to assess the feasibility of peak-to-peak ratio regression, partial least squares regression, support vector machine regression, and non-negative matrix factorization for the quantitative analysis of mixtures. Chemometrics methods were used to model and analyze the interference data in the interferogram domain, and variational mode decomposition was used to extract features from the interference data, further improving the interference data’s modeling and prediction accuracy. The results demonstrate that modal intensities obtained through variational mode decomposition of interference data improve the prediction accuracy of regression analysis. Support vector regression exhibited the most favorable predictive performance among the tested models. The root mean square error of cross-validation was reduced from 5.55% to 2.64%, and the prediction root mean square error was reduced from 5.08% to 1.5%. The improved model utilizing interference data showed higher fitting accuracy and more precise sample predictions compared to spectral data modeling.

Monitoring thermal stability of pure and adulterated sesame oil using Fourier transform infrared spectroscopy and chemometric analysis

This work investigates the application of Fourier Transform Infrared spectroscopy (FTIR) combined with chemometrics as a rapid and non-destructive tool for monitoring the thermal stability of pure sesame oil (SeO) as well as SeO blends with adulterants, including corn, soybean, and sunflower oils in different proportions (99 + 1, 95 + 5, 90 + 10, and 80 + 20 in volume). The oil samples were subjected to thermal treatment at five temperatures (25, 60, 100, 150, and 180 °C) as a function of time up to 96 h. FTIR provided sensitive approach for analysing thermal degradation and compositional changes of pure and adulterated SeO under controlled temperature conditions during varied heating times. Principal component analysis (PCA) was used to discriminate pure SeO at different treatment conditions. In addition, soft independent modelling class analogy (SIMCA) was useful to differentiate between pure and adulterated samples, and the results showed high precision and accuracy, enabling successful discrimination, with a maximum error rate of 6.00%. Finally, partial least squares regression (PLSR) models were developed and showed a good linear correlation between the FTIR signals at different conditions and the proportion of pure SeO. The root mean square of prediction (RMSEP) ranges between 0.8802 and 2.3827 with a coefficient of determination (R2) between 0.9841 and 0.8834, respectively. These findings demonstrate the efficacy of FTIR combined with chemometrics in detecting adulteration in SeO, thus offering a solution for quality control and food safety assessments in the food industry.

A rapid detection method of three foodborne pathogens based on physical and chemical Raman spectroscopy enhanced

Escherichia coli, Listeria monocytogenes, and Salmonella typhi are three pathogens commonly found in food. Label-free enhanced substrates have limitations in achieving high sensitivity in three bacteria detection. To enable low-concentration detection and differentiation of foodborne pathogens, this research presents an optimized detection strategy using Au @Ag NPs as the enhancing substrate for SERS technology. The impact of the particle size of Au @Ag NPs and the pH of the borate buffer solution on enhancing the Raman signals of these bacteria was investigated through electromagnetic and chemical enhancement mechanisms. By evaluating the intensity of bacterial Raman spectra, and employing chemometric techniques, the concentration and classification of the three bacterial species were predicted and analyzed. The research findings revealed that the optimized detection method was able to detect three pathogens at the concentration lower than 3 lg CFU/mL. Logarithmic fitting of the bacteria enabled prediction correlations above 0.98 and prediction root mean square errors below 0.17. After normalizing, efficient discrimination of low-concentration bacteria was achieved using the PLS-DA, with a classification prediction correlation greater than 0.94. The fabrication process of the proposed enhancement substrate is simple, but the stability of signal detection needs further improvement in subsequent experimental testing steps.

Degradation prediction of 65 kW proton exchange membrane fuel cells on city buses using a hybrid approach with the advantage actor-critic method

Degradation prediction of proton exchange membrane fuel cell (PEMFC) is critical for optimizing fuel cell operation and extending its lifetime, facilitating its large-scale commercialization. This paper proposed a hybrid method based on the mechanism voltage model and the advantage actor-critic (A2C) algorithm to achieve degradation prediction. A semi-empirical voltage model is established to extract four aging parameters: exchange current density, leakage current density, reaction area, and area-specific resistance. A2C is then applied to predict aging parameters and output voltage under stochastic conditions, precisely characterizing the degradation process. Two sets of ∼1,000h real data from 65 kW PEMFCs on city buses are used for training, validating, and optimizing the proposed model. The results show that the method can accurately predict voltage degradation and efficiently track voltage fluctuations without delay under actual operating conditions. The prediction root mean square errors for the two datasets with a training ratio 0.8 are 0.0095 and 0.0115, respectively, smaller than conventional prediction methods. The hybrid method can provide detailed internal degradation information and has potential for online forecast applications.

Dynamic prediction of displacement and deformation of any point on mining surface based on B-normal model.

The surface displacement and deformation of goaf caused by coal mining destroy the underground rock structure and surface ecological environment in the mining area and endanger the safety of human life and property. An accurate and efficient dynamic prediction system of mining subsidence is indispensable. Given the limited scope of the application of the probability integral model on the edge of the mobile basin, its poor prediction effect, and its low accuracy, a new mining subsidence prediction model based on the Boltzmann function is proposed. Combined with the transformed normal distribution time function, a B-normal prediction model that can predict the dynamic displacement and deformation of any point on the surface was constructed. The global optimal solution of the parameters of the dynamic prediction model was inversed by introducing particle swarm optimization shuffled frog leaping intelligent algorithm (PSO-SFLA), and then, the model was applied to the 8102 working face of the Guobei coal mine to dynamically predict the subsidence, inclination, curvature, horizontal displacement, and horizontal deformation of the goaf surface. The prediction results showed that on the strike and dip observation lines, the prediction accuracy of the dynamic subsidence and horizontal displacement of the surface could reach the centimeter level, the predicted root mean square error (RMSE) of dynamic tilt and horizontal deformation was less than 0.51 mm/m, and the predicted RMSE of dynamic curvature was within 0.020 mm/m2. The prediction results reflected the dynamic evolution law of surface displacement and deformation and verified the reliability of the B-normal dynamic prediction model, which can fully meet the needs of practical engineering applications.

Forecasting oil production in unconventional reservoirs using long short term memory network coupled support vector regression method: A case study

Production prediction is crucial for the recovery of hydrocarbon resources. However, accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data. To address this problem, a novel model combining a long short-term memory network (LSTM) and support vector regression (SVR) was proposed to forecast tight oil production. Three variables, the tubing head pressure, nozzle size, and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters. The time-series response of tight oil production was the output and was predicted by the optimized LSTM model. An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy. Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield. Decline curve analysis (DCA) methods, LSTM and artificial neural network (ANN) models were also applied in this study and compared with the LSTM-SVR model to prove its superiority. It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance. The LSTM-SVR model of Well A produced the lowest prediction root mean square error (RMSE) of 5.42, while the RMSE of Arps, PLE Duong, ANN, and LSTM were 5.84, 6.65, 5.85, 8.16, and 7.70, respectively. The RMSE of Well B of LSTM-SVR model is 0.94, while the RMSE of ANN, and LSTM were 1.48, and 2.32.

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

Objective:ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth. Material and methods:70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results:the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion:global shape features might provide an important contribution for predicting the aneurysm growth.

Calibration transfer for near-infrared (NIR) spectroscopy based on local preserving projection

Calibration transfer is a mathematical or statistical technique to solve the universality of near-infrared (NIR) spectroscopy, which is particularly important in practical applications. Therefore, a calibration transfer method based on local preserving projection (CTLPP) to correct spectral differences was proposed in this paper. Local preserving projection (LPP) is a linear feature extraction algorithm, which is a linear approximation of Laplacian Eigenmap manifold learning. It can not only overcome the shortcomings of linear feature extraction algorithms which are difficult to maintain the nonlinear manifold of the original data, but also solve the problem that nonlinear manifold learning methods are difficult to directly map new samples, which is conducive to building the conversion relationship between the source spectra and the target spectra. The transfer performance of the proposed method was evaluated in three datasets, and compared with five approaches, namely, piecewise direct standardization (PDS), calibration transfer based on independent component analysis (CTICA), spectral space transformation (SST), principal components canonical correlation analysis (PC-CCA) and calibration transfer based on neighborhood preserving embedding (CTNPE). Experimental results indicated that this proposed method can successfully correct the detection spectra among different apparatus. In addition, it can obtain relatively low prediction root mean square error (RMSEP). Therefore, the comprehensive research conducted in this work demonstrated that the proposed method is a promising calibration transfer method in NIR applications, which can provide a reference when sharing the NIR spectral models with more and more instruments.