Estimation Of Dry Matter Content Research Articles

Development and validation of near-infrared spectroscopy procedures for prediction of cassava root dry matter and amylose contents in Ugandan cassava germplasm.

Cassava utilization for food and/or industrial products depends on inherent properties of root dry matter content (DMC) and the starch fraction of amylose content (AC). Accordingly, in the present study, near-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC as critical industrial traits taking care of root sample preparation and cassava germplasm diversity available in Uganda. Upon undertaking calibrations and cross-validations, best models were adopted for validation. DMC in calibration samples ranged from 20 to 45 g 100g-1, whereas, for amylose content, it ranged from 14 to 33 g 100g-1. In the validation set, average DMC was 29.5 g 100g-1, whereas, for amylose content, it was 24.64 g 100g-1. For DMC, a modified partial least square regression model had regression coefficients (R2) of 0.98 and 0.96, respectively, in the calibration and validation set. These were also associated with low bias (-0.018) and ratio of performance deviation that ranged from 4.7 to 5.0. In addition, standard error of prediction values ranged from 0.9 g 100g-1 to 1.06 g 100g-1. For AC, the regression coefficient was 0.91 for the calibration set and 0.94 for the validation set. A bias equivalent to -0.03 and a ratio of performance deviation of 4.23 were observed. These findings confirm the robustness of NIRS in the estimation of dry matter content and amylose content in cassava roots and thus justify its use in routine cassava breeding operations. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Infrared spectroscopy for the quality assessment of Habanero chilli: a proof-of-concept study

Habanero chillies (Capsicum chinense cv Habanero) are a popular species of hot chilli in Australia, with production steadily increasing. However, there is limited research on this crop due to its relatively low levels of production at present. Rapid methods of assessing fruit quality could be greatly beneficial both for quality assurance purposes and for use in breeding programs or experimental growing trials. This work investigated the potential of using infrared spectroscopy for the quality assessment of 20 Australian Habanero chilli samples, including the prediction of dry matter content, total phenolic content, ferric reducing antioxidant potential and capsaicin/dihydrocapsaicin content. Near-infrared spectra (908–1676 nm) taken from the fresh fruit showed strong potential for the estimation of dry matter content, with a root mean standard error of cross-validation (RMSECV) of 0.50% FW. A moving-window partial least squares regression model was applied to optimise the spectral window used for dry matter content prediction, with the best-performing window being between 1224 and 1422 nm. However, the near-infrared spectra of fresh fruit could not be used to estimate the total phenolic content or capsaicin/dihydrocapsaicin content of the samples. Mid-infrared spectra (4000 − 400 cm− 1) collected from the dried, powdered material showed slightly more promise for the prediction of total phenolics and the ratio of capsaicin:dihydrocapsaicin, with a R2cv of 0.45 and RMSECV of 0.32 for the latter parameter. Although further refinement is required, this method may be able to detect samples with high/low contents of total phenolics or for the estimation of the capsaicin:dihydrocapsaicin ratio.

Review on Breeding Potato (Solanum tuberosum L.) Genotypes for Processing Quality Traits

Potato (Solanum tuberosum L .) is a rich source of protein, energy, vitamins and minerals. Potato is in the family Solanaceae with chromosome numbers vary from diploid (2n = 2x = 24) to hexaploid (2n = 6x = 72). In Ethiopia, the major objectives of potato breeding was targeted at selection of clones for high yield, wide adaptability and resistance for late blight, hence processing qualitative traits were not the main targets in potato breeding program still near future. Therefore, the objective of this review article is to discuss breeding potato genotypes for desirable processing quality traits. Tuber shape is a syndrome of many characters that considers the length/width ratio for describing the overall shape; it varies from compressed/round to long. The yellow pigment in potato tuber flesh is caused by various carotenoids that may protect against cancer, cardiovascular disease, and macular eye degeneration. The genetics of morphological traits are Tuber flesh color, skin color and tuber shape and eye depth. To minimize waste, varieties with long tubers are preferred for French fries and varieties with round tubers are ideal for crisps. Eye depth is an important trait of tuber quality because deep eyes affect the appearance of tubers and add to the cost of peeling in processing factories. Tuber quality traits in potato are specific gravity, dry mater content, starch content, Glycolalkaloids and tuber size uniformity. Specific gravity of potatoes is commonly used by the potato processing industry as a tool for quick estimation of dry matter content, as both are highly correlated. Tuber dry matter content and specific gravity significantly influenced by the interaction effect of growing environment and cultivars. Processing quality usually has relatively low to moderately high heritability, depending on the progenitor materials. Therefore, multiple location tests over several years are often required in order to determine whether a clone has indeed a high processing quality. Keywords: Potato breeding; Tuber quality; Dry mater content; Specific gravity; Processing. DOI: 10.7176/JNSR/12-13-03 Publication date: July 31 st 2021

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water’s interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.

Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content. II. Local PLS and nonlinear models

Abstract A range of modelling techniques were used in the estimation of dry matter content of intact mango fruit from short wave near infrared spectra, collected using an interactance geometry, with models developed on a data set collected across three seasons (n = 10,243) and tested on that of a fourth season (n = 1,448). Model types included Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Local Optimized by Variance Regression (LOVR), Local Partial Least Squares Regression (LPLS), Local PLS Scores (LPLS-S) and Memory Based Learner (MBL), with manual tuning of parameters undertaken. Additionally, two commercially available cloud-based chemometric packages for automated model development were trialled. All of these models gave a better result than use of a global PLS model. The best result (lowest RMSEP) was achieved with an ensemble of ANN, GPR and LPLS-S, with the best individual model result achieved by LOVR, with RMSEP of 0.839 % and 0.881 %, respectively, compared to the global PLS result of 1.014 %. The best precision was achieved with the LPLS model, with a SEP of 0.846 %, compared to the global PLS result of 1.012 %. LOVR was twice as fast as a generalized latent variable selection method LPLS-S-cv in prediction of independent validation set (at 58.7 × 10−3 s compared to 163 x 10-3 s). The ANN model was satisfactory in all categories (prediction speed, model build speed, and prediction statistics) and insensitive to tuning, e.g., 33 of the 70 parameter combinations were within 0.05 units of RMSEP of the minimum combination. However, the ANN learning rate was low. For applications that require ‘real-time’ prediction, such as fruit packlines, use of ANN and GPR models is recommended. For non-cloud based handheld NIR devices lacking the computational power to perform local modelling, ANN is recommended, and LOVR or a model ensemble recommended in cloud based implementation. The automated cloud-based systems performed well (RMSEP of 0.850 % and 0.963 % for Hone Create and DataRobot, ensemble models, respectively), without human intervention for the choosing and tuning of models.

Near-infrared Spectroscopy for Rapid Estimation of Dry Matter Content in Whole Unpeeled Potato Tubers

<p>The dry matter is one of the main quality parameters of raw and processed potatoes. In the present study, the potential of utilizing high throughput commercially available NIR interactance systems for dry matter determination in whole unpeeled potato tubers is investigated. The performance of a 2D NIR interactance instrument was compared with that of a 1D NIR interactance instrument and a traditional underwater weight apparatus. A total of 114 tubers were assessed individually with both of the NIR instruments (760-1040 nm), the underwater weight and an external reference method (freeze drying). The 1D interactance instrument obtained better prediction results than what the 2D instrument could achieve (R<sup>2</sup>=0.95, RMSECV=0.91, and R<sup>2</sup>=0.83, RMSECV=1.65, respectively). The underwater weight obtained the highest explained variance (R<sup>2</sup>=0.97), but the estimation was biased by approximately 1.5% (by weight). The poorer prediction performance of the 2D NIR interactance system can be partly explained by the lower penetration depths of the light compared to the 1D NIR interactance systems.</p>

Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion

This work applies remote sensing techniques to estimate dry matter (DM) content in tree leaves. Two methods were used to estimate DM content: a normalized index obtained from the radiative transfer model (RTM) leaf optical properties spectra (PROSPECT) in direct mode and the inversion of the PROSPECT model. The data were obtained from the Leaf Optical Properties Experiment 93 (LOPEX93) database, and only 11 species were used in this study. The species selection was based mainly on the availability of data on fresh and dry samples. The estimation of DM content was obtained from an exponential function that correlated the values of the index proposed, (R2305 − R1495)/(R2305 + R1495), against the DM content of fresh and dry leaf samples. The determination coefficient obtained (r 2 = 0.672) was higher than the coefficient obtained from the inversion of the PROSPECT model (r 2 = 0.507). The data set used to validate the normalized index was provided by the Accelerated Canopy Chemistry Program (ACCP). The determination coefficient between the values obtained from ACCP data and the values estimated for the normalized index was r 2 = 0.767.

Estimating dry matter content of fresh leaves from the residuals between leaf and water reflectance

At about 1720 nm wavelength, there is an absorption feature of leaf dry matter based on a C−H stretch overtone, which is difficult to detect in fresh green leaves because of the absorption spectrum of liquid water. We applied a method originally proposed in Remote Sensing of Environment by B.-C. Gao and A.F.H. Goetz (Estimating dry matter content of fresh leaves from the residuals between leaf and water reflectance, pp. 137–145, 1994) that used linear regression between the natural logarithm of leaf spectral reflectance and the specific absorption coefficient of liquid water over wavelengths from 1500 to 1800 nm to calculate an expected reflectance spectrum. The residual difference between the measured and expected leaf reflectance spectra enhanced the absorption feature of dry matter. The absorption feature was quantified using the normalized dry matter index (NDMI) based on the contrast between the residual leaf reflectance at 1649 and 1722 nm. NDMI was linearly related to leaf dry matter content (g cm−2) for data obtained in the field (coefficient of determination r 2 = 0.636) and for the leaf optical properties experiment (LOPEX; r 2 = 0.684). Lignin and cellulose contents were also measured during LOPEX, but NDMI was weakly correlated to these variables, indicating NDMI was sensitive to all leaf biochemical constituents. Estimation of dry matter content combined with estimates of water content will facilitate the calculation of the fuel moisture content for prediction of wildfire.

Specific gravity and dry matter content of potato

Data obtained from 516 pairs of specific gravity and dry matter determinations were used to derive a relationship for estimation of dry matter content of potatoes grown in India.

Influence of intercellular space differences due to variety and storage upon tuber specific gravity-dry matter relationship

At each of two locations in two years, five varieties of potatoes were examined for specific gravity—intercellular space relationships. At harvest intercellular space differred consistently among varieties and decreased during two months' storage at 40 F. Changes in weight, volume, and intercellular space were used to explain that changes in tuber specific gravity may or may not reflect changes in dry matter content and that differences and changes in intercellular space are of sufficient magnitude to account for most of the discrepancies between recently published regressions of dry matter on specific gravity developed without correction for intercellular space. A coordinated study is proposed to establish the correctness of this postulation. Estimation of dry matter content of white potatoes from specific gravity determinations is a widely accepted practice. The accuracy of the estimation has been questioned because of the relatively large error of estimation (±1.5 to 2%) usually encountered (24), the wide divergence of regressions (1, 6, 15, 16, 18, 19, 23), and the frequency with which discrepancies have been noted between specific gravity and quality (14). Recent recommendations call for “Improved accuracy in the quick estimation of solids either by specific gravity or other means” and clarification of the “effect of temperature and period of storage on changes in ... specific gravity” (25). The tests reported here were undertaken to obtain corroborating evidence for the estimation of dry matter content of sweet potatoes from root specific gravity after correction for intercellular space (IS), (8, 12). Sweet potatoes, as well as several other commodities, contain relatively large amounts of IS (8, 11) but when this is accounted for, a reasonably consistent relationship exists that appears to be nearly identical to the regression for the specific gravity of sucrose solutions (8). The work with sweet potatoes showed that 1 ml of IS produced a buoyancy equivalent to the weight in water of 2.38 g of dry matter (12). Although white potatoes contain relatively small amounts of IS on the average, amounts of 1% (1ml/100ml) and more have been reported (2, 8, 15, 16). Nissen, in Denmark, reported that varieties differed in IS and that for greatest accuracy the IS should be accounted for (15, 16). Most workers in the United States, however, have ignored the influence of IS upon the accuracy of the methol of estimating dry matter content from tuber specific gravity, although several have speculated that IS contributed to variation in this relationship (1, 18). The tests reported here indicate that varieties may differ with reasonable consistency in IS at harvest in much the same manner as sweet potatoes (17), and that these differences and changes in IS during storage may be large enough to account for most of the discrepancies observed in the relationship between tuber specific gravity and dry matter content.

Specific gravity — Dry matter relationship in potatoes

1. The specific gravity-dry matter relationship of potatoes grown in North Carolina was determined. 2. Location of production and variety of potato had a statistically significant effect on the specific gravity-dry matter relationship. Size of tuber was not significant but appeared to be somewhat greater than the effect of variety. These factors, though in some cases statistically significant, did not greatly affect predicted dry matter values. 3. A prediction equation and a prediction error were given for the estimation of dry matter content of potatoes from specific gravity measurements irrespective of size of tuber, variety or location grown in North Carolina.