Starch content is an important parameter indicating the state of harvest maturity of fresh cassava root. Nowadays, the methods used for estimating the starch content in the field are the measurement of root weight, size, or snapping force. These methods are simple but the results are rather incorrect. For this reason, a developed portable visible and near-infrared spectrometer­(350–1050 nm) was used to estimate rapidly and nondestructively starch content in fresh cassava root. The best starch prediction model received from the full wavelength region was able to predict the starch content with a correlation coefficient of prediction (r p) of 0.825, standard errors of prediction of 2.502%, and bias of −0.115%. Moreover, the predicted values were not significantly different from the actual values obtained from the standard method at 95% confidence intervals. It was also noted that the top position of the root was a good representative for starch prediction. In addition, this position was easy to be measured in the field before harvesting.

Short-wavelength near infrared spectra in the interactance mode were collected from intact cassava roots and cassava flesh, using two portable spectrometers for the spectral regions of 720–1050 and 850–1150 nm, respectively. All starch prediction models were developed using the partial least squares regression. Good prediction performance was obtained from the cassava flesh (cross-section cut root) measurement with a correlation of prediction (r p) of 0.917 and standard error of prediction (SEP) of 1.73%, for both spectrometers. For the intact root, the prediction models were satisfactorily accurate with r p values of 0.687 and 0.772 and SEP of 3.151 and 2.803%, respectively. Moreover, the performance measurement of all optimum models was also evaluated according to ISO 12099:2017­(E). The results showed that the predicted values were not significantly different from the actual values obtained from the standard method at 95% confidence intervals. These results showed the feasibility of using portable spectrometers to predict the starch content of fresh cassava roots.

Near-infrared (NIRS) spectroscopy, coupled with partial least squares regression, was used to predict adenosine and cordycepin concentrations in fruiting bodies of Cordyceps militaris . The fruiting body samples were prepared in four different sample formats, which were intact fruiting bodies, chopped fruiting bodies, dried powder, and dried crude extract. The actual amount of the adenosine and cordycepin concentrations in fresh fruiting bodies was analyzed by high-performance liquid chromatography. Results showed that the prediction models developed from the chopped samples provided excellent accuracy in both parameters with minimal sample preparation. These optimum models provided a coefficient of determination of prediction, standard error of prediction, bias, and residual predictive deviation, which were respectively 0.95, 16.60 mg kg –1 , –8.57 mg kg –1 , and 5.04 for adenosine prediction, and 0.98, 181.56 mg kg –1 , –1.05 mg kg –1 , and 8.9 for cordycepin prediction. The accuracy and performance of the model were determined by ISO12099:2017(E). It was found that these two equations can be considered to be acceptable at a probability level of 95% confidence. The NIRS technique, therefore, has the potential to be an objective method for determining the adenosine and cordycepin concentrations in C. militaris fruiting bodies.

The main goal of this study was to predict the age-after-harvest of milled rice and classify it for stale or fresh rice during storage by determining the thiobarbituric acid (TBA) value non-destructively via a hyperspectral imaging (HSI). Thai jasmine rice (KDML 105 variety) was stored at 25°C, 35°C, and 50°C and randomly sampled every month for 12 months for TBA testing (for 4 months at 50°C). During storage, the chemical analysis value of TBA increased over the storage time at all storage temperatures. Hyperspectral imaging in the range 864–1695 nm was used, and partial least squares regression was used to develop multivariate calibration models. The resulting prediction model could approximate quantitative values for TBA with a ratio of performance to the deviation at 2.0 and the root mean square error of prediction of 3.20 μmol MDA/kg. Partial least squares discriminant analysis was conducted for quality analysis based on the TBA value. The age-after-harvest prediction model and the model for classifying stale or fresh rice effectively performed on milled rice, providing a total cross-validation accuracy of 98% and 100%, respectively.