Rapid Determination Of Moisture Content Research Articles

Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

Rapid Determination of Moisture Content of Soil Using Microwave Oven

Rapid Determination of Moisture Content of Soil Using Microwave Oven

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion.

For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species. This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR). Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials. For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model. Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods. This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds

Moisture content is a crucial factor affecting the quality of soybean seeds. However, the determination of moisture content of soybean seeds is time-consuming and expensive. In this study, visible-near-infrared hyperspectral imaging technology (400–1000 nm) coupled with wavelength selection algorithm was applied to determine the moisture content of soybean seeds. Hyperspectral images of 96 soybean samples were obtained, and the sample set partitioning based on joint x-y distance algorithm was used to divide the calibration and prediction sets after removing outliers. Then, partial least squares regression (PLSR) models based on the original and preprocessing spectra were established, and the prediction effect of the original spectra was better than that of the preprocessing spectra. Five wavelength selection algorithms were used to select feature wavelengths to optimize the models further. Each wavelength selection algorithm was run 100 times independently to investigate its stability. The PLSR models were established based on the results of wavelength selection, and the prediction effects of all models were statistically analyzed. Results showed that the combination of interval variable iterative space shrinkage approach and successive projections algorithm (IVISSA-SPA) based on the original spectra was the most suitable model for the determination of moisture content of soybean seeds. The prediction accuracies of the IVISSA-SPA model were R2P = 0.9713 ± 0.0044, RMSEP = 0.307 ± 0.021 and RPD = 6.058 ± 0.344 in 100 independent experiments. Results indicated that visible-near-infrared hyperspectral imaging coupled with wavelength selection algorithm provided a rapid method for determining the moisture content of soybean seeds.

Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods

Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.

Evaluation of mercury content in combustible tobacco products by employing cold vapor atomic absorption spectroscopy and considering the moisture content: a comprehensive study

Plants are mainly made up of water, which constitutes between 80 and 90% of their weight. Moisture factor comes across as one of the most important in tobacco products. Rapid determination of moisture content in tobacco products comes at necessity in any tobacco management plants (before and after production). Therefore, the concern has been raised in this study to evaluate the moisture content in four kinds of combustible tobacco products using the gravimetric method. In addition, a total mercury content using cold vapour atomic absorption spectroscopy has been evaluated in all chosen combustible tobacco products in this study. Determining moisture content in four types of tobacco products does not show significant differences within a product group. Moisture content ranged from 7.9% in bidis to 25% in pipe tobaccos. Mercury content in tobacco ranged from 13 µg/kg to 32 µg/kg while in cigarette wrapper paper and filter from the limit of detection (LOD) < 1.3 µg/kg to 8.2 µg/kg. As evidenced, cigarette wrapper paper and filters alone are unlikely to be a significant source of consumer exposure to mercury. However, the proposed sample preparation method provides good results for the preparation of specific material, such as tobacco products.Graphical abstract

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

Micro-FTIR spectroscopy and partial least-squares regression for rapid determination of moisture content of nanogram-scaled heat-treated wood

Moisture sorption has a significant impact on the performance of heat-treated wood. In order to better characterize moisture sorption of heat-treated wood, a method for rapid determination of moisture content (MC) of nanogram-scaled heat-treated wood is proposed in this work. During moisture adsorption process, micro-Fourier transform infrared (FTIR) spectra of heat-treated wood were recorded. Spectral analysis was applied to these measured spectra, and then moisture adsorption sites and spectral ranges affected by moisture sorption were identified. Meanwhile, moisture contents (MCs) of heat-treated wood at various relative humidity (RH) levels were measured by using dynamic vapor sorption (DVS) setup. Based on these spectral ranges and MCs, a quantitative forecasting model was established using partial least-square regression (PLSR). Furthermore, the developed forecasting model was applied to acquire moisture sorption isotherm of heat-treated wood, in which a very positive correlation between predicted and measured MCs was observed. It was confirmed that this method was effective for rapid detection of MC of nanogram-scaled heat-treated wood which had unique advantages of rapid analysis (second level) and less sample consumption (nanogram level).

Rapid determination of moisture content in compound fertilizer using visible and near infrared spectroscopy combined with chemometrics

Moisture is a very important index for quality control of compound fertilizer. In this study, a rapid method to determine moisture content in compound fertilizer using visible and near infrared (Vis-NIR) spectroscopy combined with chemometrics is proposed. Continuous wavelet transform (CWT) with Daubechies family was used to preprocess the spectra of compound fertilizer. CWT was optimized by comparison the performance of partial least squares regression (PLSR) models. For comparison, raw spectra and three common pre-processing transforms, namely multiplicative scatter correction (MSC), first derivative (FD) and second derivative (SD) with Savitzky-Golay (SG) smoothing, were also carried out. Successive projections algorithm (SPA) was employed to select effective wavelengths with moisture under optimized continuous wavelet transforms. CWT-SPA-PLSR models were built with the effective wavelengths selected by SPA. Furthermore, CWT-SPA-LS-SVM model was developed with the best effective wavelengths. The results indicated that the optimized continuous wavelet transforms (db3 at scale 7, db4 at scale7 and db4 at scale 10) were superior; the best effective wavelengths were selected by SPA under db4 at scale 10; the performance of the CWT-SPA-LS-SVM model (R2 = 0.8054, RMSEP = 0.1795, RPD = 2.2672) is better than that of CWT-SPA-PLSR model (R2 = 0.7863, RMSEP = 0.1958, RPD = 2.0781). This study demonstrated visible and near infrared spectroscopy using continuous wavelet transform combined with successive projections algorithm and linear regression (PLSR) or nonlinear regression (LS-SVM) to determine moisture content in compound fertilizer was feasible.

Evaluation of moisture content in processed apple chips using NIRS and wavelength selection techniques

Rapid determination of moisture content during processing period of apple chips is of great significance for their real-time quality control. In this study, near infrared spectroscopy (NIRS) combined with chemometric techniques were used to measure moisture content of apple chips during processing period. Interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and genetic algorithm (GA) were utilized to select efficient wavelength regions, respectively, and partial least squares regression (PLSR) was applied to develop prediction model. The performance of models was evaluated by three types of parameters, including the root mean square error (RMSE), correlation coefficient (R) and the residual prediction deviation (RPD). The results demonstrated that, in contrast to classical PLS, iPLS and GA-PLS model, siPLS model for the prediction of moisture content had superior performance with RMSEC = 0.0877, Rc = 0.9540, RMSEP = 0.0627, Rp = 0.9747, and RPD = 4.37. Therefore, the application of NIRS and wavelength selection techniques in this study provides a feasibility of a non-destructive and rapid way to enhance the efficiency of quality assurance and control investment.

Rapid determination of moisture content in paper materials by multiple headspace extraction gas chromatography

This paper describes a new method for the rapid determination of the moisture content in paper materials. The method is based on multiple headspace extraction gas chromatography (MHE-GC) at a temperature above the boiling point of water, from which an integrated water loss from the tested sample due to evaporation can be measured and from which the moisture content in the sample can be determined. The results show that the new method has a good precision (with the relative standard deviation <0.96%), high sensitivity (the limit of quantitation=0.005%) and good accuracy (the relative differences <1.4%). Therefore, the method is quite suitable for many uses in research and industrial applications.

Historical development of grain moisture measurement and other food quality sensing through electrical properties

When the electrical properties of materials are well correlated with other properties or characteristics of those materials that are important, it is often possible to use properly designed instruments for rapidly measuring those nonelectrical properties of interest. This may be accomplished by the use of electric fields in sensing the electrical properties of the material and then obtaining estimates of the nonelectrical properties through appropriate correlations. In the agricultural and food industries, these kinds of measurements have been used for many years for rapid determination of moisture content in grain and seed. Research has been conducted seeking useful correlations between quality attributes of other food materials and their electrical properties, but they have generally been less successful than the determination of moisture content. In this article, an overview of the historical development of such measurement methods and techniques is presented with respect to grain and seed and other agricultural products and food materials. The article covers use of electrical resistance or conductance of grain samples, radio-frequency capacitance measurements, and microwave measurements of dielectric properties for sensing the moisture content of grain and seed. It deals with the principles involved rather than descriptions of specific instruments that have been developed for rapid measurement of grain and seed moisture content.

Analysis of Stability and Type Independence of Three Density-Independent Calibration Functions for Microwave Moisture Sensing in Shelled and Unshelled Peanuts

A microwave dielectric method was used for nondestructive and rapid determination of moisture content in shelled and unshelled peanuts of various types from transmission measurements of their relative complex permittivities in free space at 23C between 5 and 15 GHz. Moisture content was estimated, independent of bulk density, with three density-independent calibration functions and compared to standard oven moisture determinations; two of these functions are permittivity-based, and the other is expressed in terms of attenuation and phase shift. The effectiveness and stability of these three functions for type independence were evaluated over broad ranges of frequency, moisture content, and bulk density. While the performance of each function with individual type calibrations was reaffirmed, statistical analysis also showed high coefficients of determination in predictions with the combined type-independent calibrations. Therefore, with microwave moisture sensing technology, calibration equations can be used to accurately predict moisture content in peanuts with insensitivity to type; which is a characteristic lacking in today's commercial moisture meters.

Rice Moisture Imaging Using Electromagnetic Measurement Technique

Among many physical attributes that need to be tested on rice grain during the processing and storing process is the measurement of moisture content. Food technologists are interested in this attribute in order to determine how rice grain will be handled during processing and to get an indication of the product's quality. Because the dielectric constant of water is much greater than that of the dry material of grain, the dielectric constant of grain is correlated with its water content. Such correlation forms the basis for rapid determination of moisture content of rice grain electrically. Even though it provides relatively reliable moisture measurement it generally lacks spatial resolution. In this paper, the method based on electromagnetic imaging technique to map the two-dimensional moisture distribution in rice grain is presented. A quantitative image reconstruction algorithm employing the simultaneous iterative reconstruction technique to rapidly iterate to a final acceptance solution is described. Empirical functions of slowness value as well as dielectric constant dependence of moisture of rice grain at ambient temperature and at nominal frequency of 1 GHz are also discussed.

Rapid Moisture Content Determination of Macadamia Nuts with an Electronic Moisture Meter

An electronic moisture meter (Dole Model 400) was calibrated for rapid determination of moisture content of macadamia (Macadamia integrifolia Maiden and Betche) nuts. The meter was found suitable to measure the moisture content (from 9.5% to 21.5% on a wet-weight basis) of macadamia nuts with sufficient accuracy for routine use by farmers and processors. On average, the meter readings were about 0.21% ± 0.08% se lower compared to readings obtained from the standard forced-air oven technique. A moisture analysis with the meter required &lt;5 minutes compared to 72 hours by conventional oven-drying.

Microwave Drying of Soils

Abstract Drying of soil specimens in microwave ovens can permit rapid determination of moisture content; such determination is very important in field quality control of earthwork construction. A laboratory study was performed to investigate differences between soil moisture contents based on microwave oven drying and moisture contents developed from conventional drying procedures. For nine materials ranging from dense graded aggregate to highly plastic clays, differences between moisture contents obtained by the two methods were very small, in many cases not exceeding the variability between values obtained from multiple specimens of the same soil mix dried in a conventional oven.

Comparison of Moisture Determination Techniques over a Range of Sweet Corn Seed Maturities

Table 1. Mean seed moisture content of ‘Florida Staysweet’ and ‘Crisp N’ Sweet 710’ as measured by three moisture determination methods.’ Seed moisture content, an estimate of kernel maturity, is commonly used by the sweet corn industry to determine optimal harvest date. Depending on environmental conditions, moisture in sweet corn seed can drop rapidly. Therefore, a rapid determination of moisture content was necessary to study the effects of seed maturity at harvest on germination and vigor of shrunken-2 sweet corn. A moisture analyzer (model AVC-80; CEM Corp., Indian Trial, N.C.) consisting of a microwave drying oven, electronic balance, and a microprocessor was used. The microprocessor computes moisture content based on percent weight loss of the sample after it has been dried by microwave radiation. We compared the efficacy of such a moisture analyzer (MA) across a range of 40% to 70% moisture (fresh-weight basis) in shrunken-2 sweet corn seed to two standard methods used by the seed industry, the air-oven (AO) and Brown Duvel distillation (BD). The moisture range was selected based on the industry’s harvesting guidelines for shrunken-2 sweet corn seed, which maximizes field performance (50% to 55% moisture on fresh weight basis). Seed of two shrunken-2 commercial hybrids, ‘Florida Staysweet’ and ‘Crisp N’ Sweet 710’, was produced by controlled hand pollinations of field-grown parental inbreds in southern Minnesota. Four harvests from each cultivar, each within a single pollination date, were made as seed moisture reached ≈70%, 60%, 50%, and 40%. Moisture was determined using seeds cut to cob level from the

Microwave Drying to Determine the Solids Content of Milk and Cottage and Cheddar Cheese

Microwave ovens have been used in quality control laboratories for rapid determination of moisture content of dairy products. Factors that influence sample drying rate and final test result for microwave drying are quite different than factors that influence conventional drying methods. Differences in dielectric properties of samples will have a significant influence on microwave drying, but may not influence conventional drying. Sample handling methods that reduce variability in test results for milk, cottage cheese, and Cheddar cheese are discussed. Preliminary investigations indicated that there are differences in results from one microwave oven to the next, even when the same sample material was dried under the same analysis conditions. This variations may be due to a combination of differences in sample positioning, differences in wave patterns in individual oven cavities, and differences in magnetron power output with age and heavy usage. Good correlations of microwave test results with those from the standard method were obtained for milk and cottage and Cheddar cheese. Statistically significant differences between microwave results and standard method results indicated that for best agreement between laboratories it would be necessary to calibrate individual microwave ovens against a standard reference method for each type of product.

Determination of water in hydrodesulfurization catalysts by neutron capture .gamma.-ray spectrometry

Neutron capture ..gamma..-ray spectrometry has been applied to the rapid determination of moisture content in hydrodesulfurization catalysts. The water content can be obtained simultaneously during the determination of Co, Mo, or Ni in the same sample. The use of titanium oxide as an internal standard yielded satisfactory results and the method was independent of flux inhomogeneities and sample geometry or matrix effect. The results were in agreement with those obtained by thermogravimetric analysis. A relative standard deviation of 3.6% was obtained for a water content of 12.65%. Correlation was made between this method and the oven-drying technique.

Use of a microwave oven for the rapid determination of moisture content of soils: M.D. Ryley. Rep. Min. Trans. Rd Res. Lab., LR 280, 14 pp. (1969). [Road Res. Lab., Crowthorne, Berks, England

Use of a microwave oven for the rapid determination of moisture content of soils: M.D. Ryley. Rep. Min. Trans. Rd Res. Lab., LR 280, 14 pp. (1969). [Road Res. Lab., Crowthorne, Berks, England