Prediction Of Moisture Content Research Articles

Enhancing prior-knowledge-based control with deep attention neural network for outlet moisture content of cut-tobacco

Monitoring and controlling the drying process is crucial, particularly for rotary dryers, to ensure optimal quality and efficiency. However, due to the process’s nonlinear behavior, delays, and input disturbances, achieving this can be quite challenging. Previous research has not adequately explored online forecasting for rotary dryer control in real production settings. To bridge this gap, our study focuses on tobacco drying in an actual plant and presents a novel online prediction model for moisture content. We conducted comparative experiments to validate the model’s practical application in production settings and demonstrate its effectiveness in enhancing drying control. Our approach, called MCTFormer, has proven to be scalable to high-dimensional problems. The results of our study indicate a 45% improvement in product quality through accurate prediction of outlet moisture content, better control over moisture levels, and reduced energy consumption.

Integrating Machine Learning for Improved Prediction of Temperature and Moisture in Pavement Granular Layers

Abstract Pavement temperature and moisture content within the base and subgrade layers affect the load-bearing capacity of the pavement and dominate the pavement performance in cold regions. Accurately predicting pavement temperature and moisture content can improve pavement design and management. Conventional approaches, including numerical and statistical models, have been implemented to predict pavement temperature and soil moisture content. However, they have weaknesses, such as being only suitable for warm regions or only for predicting pavement temperature within the asphalt layer. Furthermore, none of them can simultaneously predict the pavement temperature and moisture content. To address this issue, data collected from an instrumented test road in Alberta, Canada, were used to train a model to predict the daily average pavement temperature and moisture content at various depths through three parameters, namely depth, day of the year, and air temperature. The MATLAB toolbox, Neural Net Fitting, was used, and the performance of three built-in algorithms, Levenberg–Marquardt, Bayesian regularization, and Scaled conjugate gradient backpropagation, was compared. The model with Bayesian regularization showed the highest accuracy, with an R2 value of 0.99 and a root mean square error (RMSE) of 1.49°C for pavement temperature prediction, and an R2 value of 0.95 and an RMSE of 0.025 m3/m3 for moisture content prediction. The model developed in this research is the first to simultaneously estimate pavement temperature and moisture content, so its performance was separately compared with two existing models in the literature. The artificial neural network (ANN) model shows higher accuracy than the two existing models, so it was found that the ANN could be a robust method for pavement temperature and moisture content prediction at various depths.

Prediction of Soil Moisture Content for Growth Environment Diagnosis of Soybean Cultivation Field using Partial Least Squares Regression (PLSR) Model

Prediction of Soil Moisture Content for Growth Environment Diagnosis of Soybean Cultivation Field using Partial Least Squares Regression (PLSR) Model

Research on Outgoing Moisture Content Prediction Models of Corn Drying Process Based on Sensitive Variables

Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study obtains 18 kinds of temperature and humidity variables during the drying process and uses Uninformative Variable Elimination (UVE) method to screen sensitive variables affecting the outgoing moisture content. Subsequently, six prediction models for the outgoing corn moisture content were developed, innovatively incorporating Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The results show that eight sensitive variables have been screened to predict the moisture content of outgoing corn. The sensitive variables effectively reduced the redundancy and multicollinearity of data in the MLR model and improved the coefficient of determination (R2) of ELM and LSTM models by 0.02 and 0.05. The MLR prediction model established based on the full set of temperature and humidity data has an R2 of 0.910 and a root-mean-square error (RMSE) of 0.881%, while the UVE-ELM and UVE-LSTM prediction models achieve a better fitting effect and prediction accuracy. The UVE-LSTM model is set with a batch size of 30, a learning rate of 0.01, and 100 iterations. For the training set of UVE-LSTM, the R2 value is 0.931 and the RMSE value is 0.711%. The UVE-ELM model, with sigmoid as the activation function and 14 neurons configured, runs fast and has the best prediction accuracy. The R2 values of UVE-ELM training set and validation set are 0.943 and 0.946, respectively, and the RMSEs are 0.544% and 0.581%. The models proposed in this study provide data reference and technical support for process optimization and automation control of the corn drying process.

Rapid and non-destructive prediction of moisture content on intact gayo coffee beans using near infrared spectroscopy

Abstract Presented work explores the potential of Near-Infrared Spectroscopy (NIRS) as a rapid, non-destructive analytical method for predicting moisture content in intact Gayo coffee beans. The moisture content is an essential parameter affecting the quality, processing, and shelf life of coffee beans. While conventional moisture determination techniques involve invasive procedures and considerable time, NIRS emanates as an innovative technique offering non-invasive, swift analysis. Using a robust representative selection of Gayo coffee bean samples, we acquired numerous spectra and employed multivariate analysis to construct a calibration model correlating the spectral data with reference moisture values. The initial results exhibit a high correlation coefficient (r) of 0.93 and ratio prediction to deviation (RPD) index of 3, suggesting that the NIRS technique can effectively predict the moisture content within the beans. This research underpins the potential usage of NIRS which, if validated through further testing, could transform quality control operations in the coffee industry by offering a real-time, non-destructive approach for moisture content determination.

Rapid assessment of vanilla (Vanilla planifolia) quality parameters using portable near-infrared spectroscopy combined with random forest

The determination of moisture and vanillin content significantly influences the quality of vanilla. Currently, conventional chemical methods employed for assessing these parameters are time-consuming, involve complex sample preparation, are expensive, and environmentally unfriendly due to the use of chemical solutions. Portable Near-Infrared (NIR) spectroscopy emerges as a promising alternative, characterized by smaller dimensions and lower costs. This study investigates the performance of two portable NIR spectrometers with distinct wavelengths at 740–1070 nm and 1350–2550 nm, in conjunction with Random Forest (RF) and Partial Least Square (PLS) regression, and preprocessing techniques including min-max normalization, 1st derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), 1st derivative + SNV, and 1st derivative + MSC for predicting moisture and vanillin content. At the wavelength range of 1350–2550 nm, RF coupled with 1st derivative produced the best moisture content prediction model with an R2 of 0.971, and RF paired with 1st derivative+SNV yielded the best vanillin content prediction with an R2 of 0.983. This work highlights that the integration of portable NIR and RF allows for rapid and non-destructive detection of moisture and vanillin content. This methodology provides a novel regression method for predicting vanilla qualities.

Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.

Electrical Properties for Non-destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit

FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics.

Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower's planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky-Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.

Electro-Optics Properties of Intact Cocoa Beans based on Near Infrared Technology

This study encapsulates the efficient prediction of moisture content in cocoa beans through Near Infrared Spectroscopy (NIRS) and Partial Least Squares (PLS) regression, showcasing a strong model fit with a high R square value of 0.92 and low Root Mean Square Error (RMSE) of 0.36% in calibration; these values underscore the model's accurate estimation of moisture levels. In the realm of electro-optics properties, this success highlights NIRS's capability in assessing key attributes like moisture content in cocoa beans based on their unique spectral signatures, emphasizing the technology's role in quality control for chocolate production. Furthermore, the precise predictions align with the broader objective of leveraging NIRS to evaluate and optimize the electro- optics properties of cocoa beans, fostering informed decision-making for enhanced processing and quality assurance in the cocoa industry.

Visualisation of moisture content distribution maps and classification of freshness level of loquats

Moisture content (MC) and freshness undergo evident changes during the storage of loquat fruits and thus are important indices of loquat fruit quality. Conventional methods for assessing MC and freshness are typically destructive and time-consuming, and cannot provide distribution maps of MC in the whole loquats. In this study, the use of hyperspectral imaging (HSI) was investigated for rapid and non-destructive prediction of MC in loquats, and for the visualisation of their MC distribution maps. Furthermore, freshness level discrimination models for loquats at different storage times were established using partial least squares discriminant analysis (PLS-DA), multinomial logistic regression (MLogR), and back-propagation neural network (BPNN) classification models. MC was well-predicted, with the best residual predictive deviation (RPD) of 2.40. Using the best prediction model, the distribution maps of MC in loquats were visualised. Additionally, the competitive adaptive reweighted sampling (CARS)-BPNN model obtained superior discrimination performance, with 95.56 % and 93.33 % accuracy for the calibration and prediction sets. Our results show that HSI is a promising fast and non-destructive method for determining MC and freshness of loquats.

Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

Accurate and real-time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0–10 cm [SMC10], 0–20 cm [SMC20], 0–30 cm [SMC30], 0–40 cm [SMC40], 0–50 cm [SMC50], 0–60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R2 value of 0.963 ± 0.030 and low RMSE values of 0.238 ± 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R2 and RMSE values of 0.963 ± 0.024 and 0.117 ± 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R2 and RMSE of 0.782 ± 0.090 and 0.037 ± 0.011) compared to SMC10 and SMC20 (with R2 and RMSE of 0.765 ± 0.097 and 0.056 ± 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.

Prediction of moisture content for a single maize kernel based on viscoelastic properties.

The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties. Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency-sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force-time graphs and viscoelastic parameters were extracted based on the four-element Maxwell model. Then, viscoelastic parameters and data of force-time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and featuretime variables on model accuracy was explored based on force-time graphs. The results indicate that models utilizing the force-time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S-G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively. This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry.

A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil

Deep soil moisture data have wide applications in fields such as engineering construction and agricultural production. Therefore, achieving the real-time monitoring of deep soil moisture is of significant importance. Current soil monitoring methods face challenges in conducting the large-scale, real-time monitoring of deep soil moisture. This paper innovatively proposes a real-time prediction approach to deep soil moisture combining GNSS-R data and a water movement model in unsaturated soil. This approach, built upon surface soil moisture data retrieved from GNSS-R signal inversion, integrates soil–water characteristics and soil moisture values at a depth of 1 m. By employing a deep soil moisture content prediction model, it provides predictions of soil moisture at depths from 0 to 1 m, thus realizing the large-scale, real-time dynamic monitoring of deep soil moisture. The proposed approach was validated in a study area in Goodwell, Texas County, Oklahoma, USA. Predicted values of soil moisture at a randomly selected location in the study area at depths of 0.1 m, 0.2 m, 0.5 m, and 1 m were compared with ground truth values for the period from 25 October to 19 November 2023. The results indicated that the relative error (δ) was controlled within the range of ±14%. The mean square error (MSE) ranged from 2.90 × 10−5 to 1.88 × 10−4, and the coefficient of determination (R2) ranged from 82.45% to 89.88%, indicating an overall high level of fitting between the predicted values and ground truth data. This validates the feasibility of the proposed approach, which has the potential to play a crucial role in agricultural production, geological disaster management, engineering construction, and heritage site preservation.

Stem moisture content prediction model for Larix olgensis based on beta regression.

To investigate the longitudinal variation patterns of sapwood, heartwood, bark and stem moisture content along the trunk of artificial Larix olgensis, we constructed mixed effect models of moisture content based on beta regression by combining the effects of sampling plot and sample trees. We used two sampling schemes to calibrate the model, without limiting the relative height (Scheme Ⅰ) and with a limiting height of less than 2 m (Scheme II). The results showed that sapwood and stem moisture content increased gradually along the trunk, heartwood moisture content decreased slightly and then increased along the trunk, and bark moisture content increased along the trunk and then levelled off before increasing. Relative height, height to crown base, stand area at breast height per hectare, age, and stand dominant height were main factors driving moisture content of L. olgensis. Scheme Ⅰ showed the stable prediction accuracy when randomly sampling moisture content measurements from 2-3 discs to calibrate the model, with the mean absolute percentage error (MAPE) of up to 7.2% for stem moisture content (randomly selected 2 discs), and the MAPE of up to 7.4%, 10.5% and 10.5% for sapwood, heartwood and bark moisture content (randomly selected 3 discs), respectively. Scheme Ⅱ was appropriate when sampling moisture content measurements from discs of 1.3 and 2 m height and the MAPE of sapwood, heartwood, bark and stem moisture content reached 7.8%, 11.0%, 10.4% and 7.1%, respectively. The prediction accuracies of all mixed effect beta regression models were better than the base model. The two-level mixed effect beta regression models, considering both plot effect and tree effect, would be suitable for predicting moisture content of each part of L. olgensis well.

Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging.

The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct drying method. The hyperspectral imaging system was employed to capture the spectral images of corn seeds within the wavelength range of 1100-2498 nm. By utilizing seven preprocessing techniques, including moving average, S-G smoothing, baseline, normalization, SNV, MSC, and detrending, we preprocessed the spectral data and then established a PLSR model for comparison. The results show that the model established using the normalization preprocessing method has the best prediction performance. To remove spectral redundancy and simplify the prediction model, we utilized SPA, CASR, and UVE algorithms to extract feature wavelengths. Based on three algorithms (PLSR, PCR, and SVM), we constructed 12 predictive models. Upon evaluating these models, it was determined that the normalization-SPA-PLSR algorithm produced the most accurate prediction. This model boasts high RC2 and RP2 values of 0.9917 and 0.9914, respectively, along with low RMSEP and RMSECV values of 0.0343 and 0.0257, respectively, indicating its exceptional stability and predictive capabilities. This suggests that the model can precisely estimate the moisture content of maize seeds. The results showed that hyperspectral imaging technology provides technical support for rapid and non-destructive prediction of corn seed moisture content and new methods in seed quality evaluation.

Control of multi-energy drying system: Optimal weighted combination prediction of moisture content and fuzzy compensation of wind speed

In order to monitor the moisture content accurately and improve the drying uniformity of the same batch of materials, an online moisture monitoring approach based on optimal weighted combination model (OWCM) was developed, and a fuzzy control strategy of wind speed compensation was established for multi-field coexistence. The Human Machine Interface (HMI) and remote monitoring modules are designed, which can automatically obtain, view and store environmental parameters on site and mobile devices. The system was loaded into a kelp drying equipment, and moisture content prediction and drying uniformity verification tests were conducted respectively. The results showed that Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the OWCM are 0.022 and 0.019, the prediction accuracy was better than that of single model and average weighted combined model (AWCM). The drying time difference between the front and back of the same batch of kelp was shortened from 1 ∼ 1.5 h to 0 ∼ 0.5 h. This study provides theoretical and technical support for the synergistic utilization of multiple energies and the research and development of aquatic products processing equipment.

Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy

Abstract The moisture content of porang powder (PP) is an inherent quality parameter. Therefore, several analytical methods, such as oven drying and Karl–Fischer titration, were applied to determine the content. However, these techniques are noted to have various disadvantages, such as being time-consuming, requiring sample preparation, being labor-intensive, and producing chemical waste. This study aims to investigate the potential of visible near-infrared (Vis-NIR) spectroscopy as a nondestructive and sustainable analytical technology to predict moisture content in PP. In this study, we developed a traditional machine learning algorithm, a partial least squares regression (PLSR), in tandem with two spectral bands, which are Vis-NIR (400–1,000 nm) and NIR (954–1,700 nm). To upgrade the performance of PLSR, we applied seven preprocessing techniques: mean normalization, maximum normalization, range normalization, multiplicative scatter correction, standard normal variate (SNV), and Savitzky–Golay first and second derivatives. We found that PLSR using NIR spectral bands was more effective; the preprocessed mean normalization exhibited the best results with a coefficient of determination ( R p 2 ) \left({R}_{p}^{2}) of 0.96 and a standard error prediction (SEP) of 0.56 using five latent variables. Furthermore, we also extracted 39 optimum wavelengths using variable importance in projection and achieved better performance ( R p 2 {R}_{p}^{2} = 0.95, SEP = 0.56%wb, and 5 LVs) via SNV preprocessed NIR spectra.