Sample temperature is well known to impact model performance for prediction of chemical attributes in high-moisture-content samples when using short-wave near infrared spectroscopy. A number of methods proposed to reduce this effect were compared in this study. A short-wave near infrared spectroscopy system operating in transflectance geometry was used to record spectra of sucrose solutions (mean = 4.16% and SD = 5.8% w/v) at different temperatures. Partial least-squares regression models were developed using spectra of sucrose solutions collected at 15°C and tested on spectra of an independent set of sucrose solutions at a sample temperature of 35°C. As sample temperature impacts the water peak, the performance of a model of sucrose content is perturbed, mainly through an increase in bias. Addition of a relatively small number of spectra of the same set of samples at different temperatures facilitated a model robust to temperature, but continued addition of samples at 15°C beyond the ratio of 1:125 overwhelmed the compensation effect, resulting in a model that was not robust to temperature. The use of orthogonal scatter correction (OSC), generalised least square weighting (GLSW), external parameter orthogonalisation (EPO) and repeatability file were considered. Of these methods, OSC corrected bias but impacted bias corrected root mean square error of prediction (SEP) and r 2 , EPO performed better but still with some bias, GLSW gave the best r 2 and SEP result but still with bias, while use of the repeatability file method gave the best overall result.

The utility of a handheld visible-short wave near infrared spectrophotometer utilising an interactance optical geometry was assessed in context of the noninvasive determination of intact tomato dry matter content, as an index of final ripe soluble solids content, and colouration, as an index of maturation to guide a decision to harvest. Partial least squares regression model robustness was demonstrated through the use of populations of different harvest dates or growing conditions for calibration and prediction. Dry matter predictions of independent populations of fruit achieved R2 ranging from 0.86 to 0.92 and bias from −0.14 to 0.03%. For a CIE a⁎ colour model, prediction R2 ranged from 0.85 to 0.96 and bias from −1.18 to −0.08. Updating the calibration model with new samples to extend range in the attribute of interest and in sample matrix is key to better prediction performance. The handheld spectrometry system is recommended for practical implementation in tomato cultivation.

The dry-extract system for (near) infrared (DESIR) technique was implemented using reflectance near-infrared spectroscopy in context of detection of contact pesticide residues on fruit. Based on chemical structure, spectra features and regression statistics for PLSR models, a product containing metiram and pyraclostrobin was chosen from six pesticides for further consideration. Regression models based on spectra of dry extracts of aqueous solutions and either acetone or water washes of contaminated fruit were encouraging (RMSECV of approximately 0.03 -0.06 mg a.i.). This level of analytical performance would support the use of the technique as a rapid screening tool, with suspect samples then subject to the reference GC-MS analysis method. However, the PLSR model performance was poor across populations of fruit, suggesting that matrix changes in the solvent wash between sets of fruit is problematic. Further work is required to establish whether sufficient variation can be built into a calibration set to overcome this issue, without degrading model performance to the point where it loses practical application.

Chinese citrus fly, Bactrocera minax (Enderlein), is one of the most important pests of citrus. The pest is more problematic in the eastern part of the country, Nepal. Because of the difficulties associated with the control of this pest by chemical insecticides, farmers had experienced great losses in Sweet Orange. Therefore, a participatory field survey was conducted under farmer field conditions to assess losses and measure the efficacy of different local and recommended management options to address the problem of this pest. Study consisted of two major parts: monitoring of pest population and farmer’s survey. For monitoring three orchards were selected located at different altitude i.e. 1200 masl, 1300 masl and 1400 masl. Great Fruit fly Bait (25% protein hydrolysate and 0.1% abamectin) in McPhail traps were used for monitoring. Monitoring was done in every 7 days interval and lures were changed in every 15 days for the effectiveness. Only 18.3% farmers were using protein bait for the management of fruit fly. B. minax had peak population intensity at May and was found decreasing after June so the management practices should be adopted before June to prevent the loss by fruit fly. However, for best effective control attention for monitoring and management procedures has to take place throughout the life cycle of the insect.

Partial least-squares regression models were developed using spectra of tomato fruit collected at 15°C and tested on spectra of an independent set of fruit at higher sample temperatures. The influence of sample temperature on the model used to predict fruit dry matter (DM) was manifested primarily in terms of bias, not standard error of prediction. For example, a model for DM created with samples at 15°C had a bias of-0.9% DM and-1.9% DM when used to predict DM in fruit at 25°C and 35°C, respectively. The addition of spectra of a relatively small number of samples collected at different temperatures to the calibration set can create a model that is robust to temperature; however, continued addition of samples at a uniform temperature overwhelms this compensation effect, resulting in a model that is not robust to temperature. For a model that included spectra of fruit at a range of temperatures, the prediction bias increased as the ratio of samples at 15°C to samples at other temperatures increased beyond 200:1. The use of orthogonal scatter correction, external parameter orthogonalisation, generalised least-square weighting, global model development and repeatability file were compared for the development of a temperature-robust DM model. The use of a repeatability file is recommended on the basis of the lowest root mean square error of prediction and bias. Selection of wavelength regions to avoid water absorption features is recommended for an attribute not associated with an OH feature (such as skin-colour prediction).