Prediction Of Dry Matter Content Research Articles

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.

Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp

The prediction of dry matter content (DMC) and soluble solids content (SSC) in durian pulp were performed using a small laboratory scale in-line visible and short wave near infrared (Vis-SWNIR) spectroscopic system. The fiber optic diode array spectrometer with a charged coupled device (CCD) detector in a wavelength range of 450−1000 nm was used for spectral data acquisition. The spectra of the sample were acquired on the moving conveyor belt in two different orientations, including scanning in the upright position of pulps collected in 2018 and the stable position by scanning on the side of the pulps collected in 2019. Partial least squares regression (PLSR) was used to establish the relationship between the spectra and observed DMC and SSC values using the different wavelength ranges, including 450−1000, 700−1000, and 800−1000 nm for the comparison. The results showed that the durian pulp should be scanned in the upright position at the center of the pulp. Moving average smoothing preprocessing combined with the standard normal variate (SNV) for DMC and multiple scatter correction (MSC) for SSC gave the best result. The suitable wavelength range for model development to predict the DMC and SSC was 700−1000 nm and 800−1000 nm, respectively. After comparing the results, the optimum model showed the coefficient of determination of calibration (RC2), and prediction (RP2), root mean square error of prediction (RMSEP), bias, and the ratio of performance to interquartile distance (RPIQ) of 0.88, 0.83, 4.32 %, 1.25 %, and 3.52 for DMC and 0.70, 0.70, 4.0 %, 0.4 %, and 2.2 for SSC prediction.

NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalisation pre-treatment

Abstract The assessment of dry matter content (DMC) as a surrogate for oil content of single olive fruit was considered in terms of spatial variation in DMC, optimisation of spectral pre-treatments, wavelength range and calibration transfer procedure. There was no consistent variation in DMC from apical to distal end of the fruit. Short wave near infrared spectra (to 1100 nm) were acquired of single fruit using an interactance geometry, with the small size of the fruit resulting in baseline variation between samples. The pre-treatment methods of first derivative (D1), second derivative (D2), standard normal variate transformation (SNV), normalized spectral ratio (NSR), variable sorting for normalization (VSN) and their combinations were applied prior to development of partial least squares regression models on DMC. Root mean square error of prediction (RMSEP) values were reduced with use of the SNV-VSN-D2 pre-treatment (by 34 and 35 %) to 0.95 and 0.88 %, for two handheld NIRS instruments, used in prediction of an external set, n = 212. The percentage of the predicted population within the limits of ± 5 % of actual values was 92.5 and 88.7 % for the VSN PLSR models and 83.0 and 77.3 % for the original models, for two instruments, respectively. Variable selection allowed a further improvement in RMSEP values (to 0.93 and 0.87 %) for the two instruments, respectively. For transfer of models between instruments, model updating based on SNV-VSN-D2 pre-treated spectra resulted in a RMSEP of 1.00 %, compared to 1.79 % for D2 pre-treated spectra, outperforming the classical calibration transfer methods of piecewise direct standardization (PDS) and spectral space transformation (SST). The results have significance to the practical implementation of NIRS-DMC estimation of olive fruit in field conditions.

Cassava yield traits predicted by genomic selection methods

Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions’ BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 –RR-BLUP to 0.4756—RKHS) and dry root yield (0.4689 –G-BLUP to 0.4818—RKHS) in comparison with dry matter content (0.5655 –BLASSO to 0.5670 –RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99–1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.

Optimized Prediction of Reducing Sugars and Dry Matter of Potato Frying by FT-NIR Spectroscopy on Peeled Tubers.

Dry matter content (DMC) and reducing sugars (glucose, fructose) contents of three potato varieties for frying (Innovator, Lady Claire, and Markies) were determined by applying Fourier-transform near-infrared spectrometry (FT-NIR), with paying particular attention to tubers preparation (unpeeled, peeled, and transversally cut tubers) before spectral acquisitions. Potatoes were subjected to normal storage temperature as it is processed in the industry (8 °C) and lower temperature inducing sugar accumulations (5 °C) for 195 and 48 days, respectively. Prediction of DMC has been successfully modeled for all varieties. A common model to the three varieties reached R2, root mean square error (RMSEP), and ratio performance to deviation (RPD) values of 0.84, 1.2, and 2.49. Prediction accuracy of reducing sugars was variety dependent. Reducing sugars were accurately predicted for Innovator (R2 = 0.84, RMSEP = 0.097, and RPD = 2.86) and Markies (R2 = 0.78, RMSEP = 0.033, and RPD = 2.15) and slightly less accurate for Lady Claire (R2 = 0.63, RMSEP = 0.036, and RPD = 1.64). The lack of accuracy obtained with the Lady Claire variety is mainly due to the tight variability in sugar content measured over the storage. Finally, the best preparation of the tuber from the point of view of the accuracy of the prediction models was to use the whole peeled potato. Such preparation allowed for the improvement in RPD values by 15% to 38% the RPD values depending on reducing sugars and 35% for DMC.

Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy

Prediction of dry matter content in whole potatoes is a desired capability in the processing industry. Accurate prediction of dry matter content may greatly reduce waste quantities and improve utilization of the raw material through sorting, hence also reducing the processing cost. The following study demonstrates the use of a low resolution, high speed NIR interactance instrument combined with partial least square regression for prediction of dry matter content in whole unpeeled potatoes. Three different measuring configurations were investigated: (1) off-line measurements with contact between the potato and the light collection tube; (2) off-line measurements without contact between the potato and the light collection tube; and (3) on-line measurements of the potatoes. The offline contact measurements gave a prediction performance of R2=0.89 and RMSECV=1.19. Similar prediction performance were obtained from the off-line non-contact measurements (R2=0.89, RMSECV=1.23). Significantly better (p=0.038) prediction performance (R2=0.92, RMSECV=1.06) was obtained with the on-line measuring configuration, thus showing the possibilities of using the instrument for on-line measurements. In addition it was shown that the dry matter distribution across the individual tuber could be predicted by the model obtained.

Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit

Fourier Transform (FT)-near infra-red spectroscopy (NIRS) was investigated as a non-invasive technique for estimating percentage (%) dry matter of whole intact ‘Hass’ avocado fruit. Partial least squares (PLS) calibration models were developed from the diffuse reflectance spectra to predict % dry matter, taking into account effects of seasonal variation. It is found that seasonal variability has a significant effect on model predictive performance for dry matter in avocados. The robustness of the calibration model, which in general limits the application for the technique, was found to increase across years (seasons) when more seasonal variability was included in the calibration set. The Rv2 and RMSEP for the single season prediction models predicting on an independent season ranged from 0.09 to 0.61 and 2.63 to 5.00, respectively, while for the two season models predicting on the third independent season, they ranged from 0.34 to 0.79 and 2.18 to 2.50, respectively. The bias for single season models predicting an independent season was as high as 4.429 but ≤1.417 for the two season combined models. The calibration model encompassing fruit from three consecutive years yielded predictive statistics of Rv2=0.89, RMSEP=1.43% dry matter with a bias of −0.021 in the range 16.1–39.7% dry matter for the validation population encompassing independent fruit from the three consecutive years. Relevant spectral information for all calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 890–980, 1005–1050, 1330–1380 and 1700–1790nm regions. These results indicate the potential of FT-NIRS, in diffuse reflectance mode, to non-invasively predict the % dry matter of whole ‘Hass’ avocado fruit and the importance of the development of a calibration model that incorporates seasonal variation.