Drug content determination of low-dosed hot-melt extruded filaments using Raman spectroscopy.

The aim of this study was to evaluate the suitability of a non-disruptive Raman spectroscopic method to quantify drug concentrations below 5 w% within a polymer matrix produced by hot-melt extrusion (HME). For calibration, praziquantel (PZQ)-polyvinylpyrrolidone-vinylacetat-copolymer (PVP-VA) mixtures were extruded. By focusing the laser light of the Raman probe to a diameter of 1 mm and implementing a self-constructed filament holder, the signal-to-noise (S/N) ratio could be reduced considerably. The obtained Raman spectra show quite high fluorescence, which is likely to be caused by dissolved pharmaceutical active ingredient (API) in the polymer matrix. For content determination, HPLC analysis was conducted as a reference method using the same filament segments. A partial least squares (PLS) model, regressing the PZQ concentrations from HPLC method analysis versus the off-line collected Raman spectra, was developed. The linear correlation for a suitable extrusion run for the production of low-dosed filaments (extrusion 1, two kneading zones) is acceptable (R2 = 0.9915) while the correlation for a extrusion set-up with low miscibility (extrusion 2; without kneading zone) is unacceptable (R2 = 0.5349). The predictive performance of the calibration model from extrusion 1 is rated by the root mean square error of estimation (RMSEE), which was 0.08%. This calibration can now be used to validate the content of low-dosed filaments during HME.

Verification of Commercial Near-Infrared Spectroscopy Measurement and Fresh Weight Diversity Modeling in Brix% for Small Tomato Fruits with Various Cultivars and Growth Conditions.

The use of non-destructive commercial near-infrared (NIR) spectroscopy to estimate Brix% was verified using all samples of cherry tomato 'TY Chika', currant tomato 'Microbeads', and the M&S or market-purchased and supplemental local source tomatoes. Additionally, the relationship between fresh weight and Brix% of all samples was examined. These tomatoes had a diversity of cultivars, growing methods, harvest timing, and production locations and varied widely from 4.0% to 14.2% for Brix% and 1.25 g to 95.84 g for fresh weight. Regardless of the diversity of all samples, it was revealed that the refractometer-based Brix% (y) was practically estimated from the NIR-derived Brix% value (x) using a relationship of y = x (RMSE = 0.747 Brix%) after only a one-time calibration for the NIR spectrometer offset. An inverse relationship between fresh weight and Brix% could be modeled using a hyperbolic curve fit, and the model showed an R2 of 0.809 except for 'Microbeads'. The Brix% of 'TY Chika' was highest on average (9.5%) and had a large difference from 6.2 to 14.2% among the samples. Data distribution of cherry tomato groups such as 'TY Chika' and M&S cherry tomatoes was closer, indicating a roughly linear correlation between fresh weight and Brix%.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image.

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants' growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems.

Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.

Pre-dispersive near-infrared light sensing in non-destructively classifying the brix of intact pineapples.

Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed pre-dispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits.

Quantification and Variability Analysis of Lignin Optical Properties for Colour-Dependent Industrial Applications.

Lignin availability has increased significantly due to the commercialization of several processes for recovery and further development of alternatives for integration into Kraft pulp mills. Also, progress in lignin characterization, understanding of its chemistry as well as processing methods have resulted in the identification of novel lignin-based products and potential derivatives, which can serve as building block chemicals. However, all these have not led to the successful commercialization of lignin-based chemicals and materials. This is because most analyses and characterizations focus only on the technical suitability and quantify only the composition, functional groups present, size and morphology. Optical properties, such as the colour, which influences the uptake by users for diverse applications, are neither taken into consideration nor analysed. This paper investigates the quantification of lignin optical properties and how they can be influenced by process operating conditions. Lignin extraction conditions were also successfully correlated to the powder colour. About 120 lignin samples were collected and the variability of their colours quantified with the CIE L*a*b* colour space. In addition, a robust and reproducible colour measurement method was developed. This work lays the foundation for identifying chromophore molecules in lignin, as a step towards correlating the colour to the functional groups and the purity.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

BACKGROUND: The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds. RESULTS: Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables. CONCLUSIONS: The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry.

Application of vibration response technique for the firmness evaluation of pear fruit during storage.

Storage conditions of pear affect its subsequent softening process and shelf life. Measurements of firmness have traditionally been carried out according to the Magness Taylor (MT) procedure; using a texture analyzer or penetrometer in reference texture tests. In this study, a non-destructive method using Laser Doppler vibrometer (LDV) technology was used to estimate texture firmness of pears. This technique was employed to detect responses to imposed vibration of intact fruit using a shaker. Vibration transmitted through the fruit to the upper surface was measured by LDV. A fast Fourier transform algorithm was used to process response signals and the desired results were extracted. Multiple Linear Regression models using fruit density and four parameters obtained from modal tests showed better correlation (R(2) = 0.803) with maximum force in Magness Taylor test compared to the models that used only modal parameters (R(2) = 0.798). The best polynomial regression models for pear firmness were based on elasticity index (EI) and damping ratio (η) with R(2) = 0.71 and R(2) = 0.64, respectively. This study shows the capability of the LDV technique and the vibration response data for predicting ripeness and modeling pear firmness and the significant advantage for commercially classifying of pears based on consumer demands.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health.

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

Probing the impact of sustainable emerging sonication and DBD plasma technologies on the quality of wheat sprouts juice.

Sonication and dielectric barrier discharge (DBD) plasma are sustainable emerging food processing technologies. The study investigates the impact of sonication, DBD-plasma, and thermal treatment (TT) on wheat sprout juice. The obtained results indicated a significant (p < 0.05) increase in chlorophyll, total phenolics, flavonoids, DPPH assay, and ORAC assay after DBD-plasma (40 V) and sonication (30 mins) treatment as compared to TT and untreated samples. Both emerging technologies significantly (p < 0.05) reduce the polyphenol oxidase and peroxidase activities, but the TT sample had the highest reduction. Moreover, the synergistic application of both technologies significantly reduced the E. coli/Coliform, aerobics, yeast and mold up to the 2 log reduction, but the TT sample had a complete reduction. DBD-plasma and sonication processing significantly decreased (p < 0.05) the particle size, reducing apparent viscosity (η) and consistency index (K); while increasing the flow behavior (n), leading to higher stability of wheat sprout juice. To assess the impact of emerging techniques on nutrient concentration, we used surface-enhance Raman spectroscopy (SERS) as an emerging method. Silver-coated gold nano-substrates were used to compare the nutritional concentration of wheat sprout juice treated with sonication, DBD-plasma, and TT-treated samples. Results showed sharp peaks for samples treated with DBD-plasma followed by sonication, untreated, and TT. The obtained results, improved quality of wheat sprout juice, and lower microbial and enzymatic loads were confirmed, showing the suitability of these sustainable processing techniques for food processing and further research.

Protein and lipid content estimation in soybeans using Raman hyperspectral imaging.

Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory-Part II: Implementation and Validation.

Eggshell strength is a critical quality factor for consumption eggs as it affects the probability of breakage in practice. In this study, a fast and low-cost methodology for the non-destructive determination of eggshell strength is presented. The method utilized a small steel ball to impact the egg and a microphone to analyse the impact characteristics. Hertz contact theory was applied to relate the measured impact characteristics to the local stiffness of the eggshell. Therefore, a total of 150 eggs were studied on which eight consecutive measurements per egg were taken around the equator at equidistant places. The results showed a strong correlation of 0.93 between the traditional static stiffness measured during quasi-static compression tests and the average stiffness obtained from the new methodology. This paves the way towards fast, low-cost and non-destructive in-line shell strength measurements to reduce the number of cracked eggs reaching the consumer.

Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer).

Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.

Image-based phenotyping to estimate anthocyanin concentrations in lettuce.

Anthocyanins provide blue, red, and purple color to fruits, vegetables, and flowers. Due to their benefits for human health and aesthetic appeal, anthocyanin content in crops affects consumer preference. Rapid, low-cost, and non-destructive phenotyping of anthocyanins is not well developed. Here, we introduce the normalized difference anthocyanin index (NDAI), which is based on the optical properties of anthocyanins: high absorptance in the green and low absorptance in the red part of the spectrum. NDAI is determined as (Ired - Igreen)/(Ired + Igreen), where I is the pixel intensity, a measure of reflectance. To test NDAI, leaf discs of two red lettuce (Lactuca sativa) cultivars 'Rouxai' and 'Teodore' with wide range of anthocyanin concentrations were imaged using a multispectral imaging system and the red and green images were used to calculate NDAI. NDAI and other commonly used indices for anthocyanin quantification were evaluated by comparing to with the measured anthocyanin concentration (n = 50). Statistical results showed that NDAI has advantages over other indices in terms of prediction of anthocyanin concentrations. Canopy NDAI, obtained using multispectral canopy imaging, was correlated (n = 108, R2 = 0.73) with the anthocyanin concentrations of the top canopy layer, which is visible in the images. Comparison of canopy NDAI from multispectral images and RGB images acquired using a Linux-based microcomputer with color camera, showed similar results in the prediction of anthocyanin concentration. Thus, a low-cost microcomputer with a camera can be used to build an automated phenotyping system for anthocyanin content.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review.

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Non-destructive investigation of sandstone blocks used in the Wat Phu temple in Laos and the Banteay Chhmar temple in Cambodia.

Sandstone blocks quarried from the late Jurassic to the early Cretaceous Red Terrane Formation were used to construct the Wat Phu temple in Laos and the Banteay Chhmar temple in Cambodia. The sandstone blocks of the Banteay Chhmar temple are gray to yellowish brown in color and their magnetic susceptibilities and Sr contents are relatively high, similar to the sandstone blocks used in the Angkor monument. In contrast, the Wat Phu temple consists of reddish sandstone blocks with significantly lower magnetic susceptibilities and Sr contents than those used in the Banteay Chhmar temple and Angkor monument. The sandstone blocks of the Banteay Chhmar temple were likely supplied from quarries in Ta Phraya, Thailand, and those of the Wat Phu temple are likely to have been supplied from the area near these temples. The Red Terrane Formation is widely distributed throughout Mainland Indochina, and most of these sandstones show low magnetic susceptibilities and low Sr contents, similar to those of the Wat Phu temple. Sandstone with high magnetic susceptibilities and high Sr contents is found in the sandstone quarries in Ta Phraya and the southeastern foothill of Mt. Kulen, which is the supply source of the sandstone blocks used in the Angkor monument, early buildings of the Bakan monument, and Banteay Chhmar temple. The sandstone with high magnetic susceptibility and high Sr content is distributed in limited areas and implies a weak degree of weathering during the sandstone formation process or a difference in the source rocks.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory Part I: Theory and Applicability.

In the egg industry, fast and highly reliable quality measurements are crucial. This study presents a novel method based on Hertz contact theory that allows for non-destructive determination of eggshell strength. The goal of the study was to evaluate the material strength (Young's Modulus) and structural strength (stiffness) of eggshells. To this end, an experimental setup was constructed to measure the collision of an eggshell with a small steel ball, which was recorded using a laser vibrometer. The study analyzed a sample of 120 eggs and found a correlation of 0.85 between the traditional static stiffness measured during quasi-static compression tests and the stiffness obtained from the Hertz contact theory. The results show that Hertz contact theory is valid for small steel spheres impacting eggshells, while a sensitivity analysis indicated that the most important factor in determining the strength of the eggshell is the contact duration between the egg and the impactor. These results open up the possibility of grading eggs based on their shell strength in a non-destructive manner.

Application of NIR spectroscopy and multivariate analysis for Non-destructive evaluation of apple moisture content during ultrasonic drying.

Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R2p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.

Effect of Different Extraction Methods on the Quality and Biochemical Attributes of Pomegranate Juice and the Application of Fourier Transformed Infrared Spectroscopy in Discriminating Between Different Extraction Methods.

This study investigated the effects of extraction methods on the physicochemical, phytochemical, and antioxidant properties of pomegranate juice (cv. Wonderful). In addition, the application of attenuated total reflectance Fourier transformed mid-infrared (ATR-FT-MIR) spectroscopy and chemometrics were explored in order to discriminate between different extraction methods. Juice variants evaluated included juice extracted without crushing the seeds (arils only) using a juice extractor (JE), juice extracted by crushing the seeds using a blender (arils plus seed) (JB), and juice extracted from half fruit using a commercial hand press juicer (CH). Juice extracted from CH had higher total soluble solid (TSS) content (18.20%), TSS/TA ratio (15.83), and color properties (a* = 32.67, b* = 11.80, C* = 34.77) compared with extraction methods JE and JB. The juice extracted from JB showed the highest titratable acidity (2.17%), cloudiness (0.43), and lowest pH value (2.69). The total phenolics and anthocyanin content in the investigated juice ranged from 1.87 to 3.04 g gallic acid equivalent (GAE)/L and 37.74-43.67 mg cyanidin 3-glucoside equivalent/L of crude juice, respectively. Juice extracted from JB and CH was significantly higher in phenolic and anthocyanin compared with JE. Orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component analysis (PCA) were used for classification. Classification accuracy of 100% was achieved between the three methods. The S-line plot revealed that the corresponding wavelength bands within the following regions 1,090, 1,250, 1,750, and 3,200 cm-1 were responsible for discrimination between the different extraction methods. Our results suggest that the main contributor to the discrimination between extraction methods were TSS, TSS/TA, color attributes, and anthocyanin content. Overall, this study has demonstrated that ATR-FT-MIR spectroscopy provides a powerful way to discriminate between juice extraction methods.

Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model.

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000-2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.