The measurement of different quality properties requires particular tools and chemical materials, most of which are time‐using. The present research was accomplished to survey the possibility of using NIRS (870–2450 nm) to predict the amylose content (AC), protein content (PC), breakdown (BDV), and setback viscosity (SBV) of white rice (Khazar variety) and its flour. Determination coefficients of calibration models to flour samples of AC, PC, BDV, and SBV generated by the partial least‐squares (PLS) regression were obtained as R2cal ≥ .85 and R2pre ≥ .80. Root mean square error of calibration (RMSEC) was calculated as 0.393, 0.07, 2.55, and 1.33, respectively. Similarly to grain samples, were obtained as R2cal ≥ .88 and R2pre ≥ .71 for calibration and prediction. RMSEC was measured as 0.303, 0.27, 2.59, and 3.11, respectively. NIRS has the potential to be used as a quick technique for predicting the quality attributes of kernel specimens.

One of the major problems in predicting the quality properties of rice is that conducting experiments can be highly expensive. Therefore, it may be necessary to explore prediction of quality properties through near‐infrared spectroscopy (NIRS). The present research was designed to survey the possibility of using NIRS to predict the Amylose Content (AC), Protein Content (PC), Breakdown Viscosity (BDV), and Setback Viscosity (SBV) of rice and its flour. R2cal to flour samples of AC, PC, BDV, and SBV produced by the Partial Least Squares (PLS) regression, were obtained as R2cal ≥ 0.887 and R2pre ≥ 0.805. Root Mean Square Error of Calibration (RMSEC) was calculated as 0.349, 0.078, 1.868, and 0.68, respectively. Similarly to rice samples, PLS regression was obtained as R2cal ≥ 0.93 and R2pre ≥ 0.75. RMSEC was measured as 0.24, 0.21, 1.87, and 2.58, respectively. The findings showed that, NIRS has the potential to be used as a quick procedure for predicting the quality properties. Practical applications All existing methods for measuring the rice quality indexes are labor‐expensive or time‐consuming. The development of more inexpensive methods can help precipitate the marketing process of the rice. The results of the present research are expected to lead to the development of NIR spectroscopic techniques for determining the quality of rice quickly through focusing on the chemical and physicochemical properties. The results can be used for designing and developing devices to test the quality indices of kernels, similar to those are being used to test the wheat quality according to single kernel characterization (SKCS).

One of the major problems in predicting the quality properties of rice is that conducting experiments in the food industry can be highly expensive. The objective of this study was to predict some quality properties in varieties (Domsiah, Hashemi, Dorfak, and Kadus) via compression test at moisture levels 9 and 14% w.b. Based on historical data design, RSM was used to model and estimate of dependent variables (amylose (AC) and protein content (PC), gelatinization temperature, gel consistency GC), minimum (Min.V), final (FV), breakdown (BDV) and setback viscosity (SBV), peak time (PT) and pasting temperature (Pa.T)) through independent variables (the rate of force, deformation, rupture energy, tangent, and secant modulus). An ANOVA test showed that models were significant (p < 0.05). The most appropriate model for response variables prediction of AC and GC (Kadus 14%), PC (Domsiah 9%), Min.V, FV, and SBV (Dorfak 9%), BDV (Dorfak 14%), PT (Hashemi 14%), and Pa.T (Kadus 9%) was Rpred2 as 0.86, 0.85, 0.93, 0.955, 0.953, 0.94, 0.94, 0.86, and 0.91, respectively, with the most appropriate optimal values as 23.52%, 48, 10%, 164.95 RVU, 304.12 RVU, 162.66 RVU, 64.52 RVU, 6.09 min, and 92.45°C and desirability as 0.91, 0.95, 0.95, 0.80, 0.89, 0.83, 0.84, 0.89, and 0.96, respectively. The optimal values of the independent variables have a decreasing trend, and the optimal values of the response variables are proportional to the optimal conditions. The results indicated that the RSM could be quite useful in the optimization of the models developed for predicting the rice quality properties.

To improve the thermal performance, storage and saving heat solar energy of conventional greenhouse, a passive solar greenhouse was built which its north wall was made of soil. The bottom part of the north, south, west and east walls were sloping and constructed below ground surface. The indoor air temperature was measured during January and February. To optimize the size of greenhouse in cold climate condition a TRNSYS model was created and validated using experimental data. According to the results obtained, Total Incident Solar Radiation (TISR) in the north wall was 484 MJ during January and February and there was the possibility of cultivation in it. More specifically, the variation of TISR during 60 days varied from 190 to 3811 kJ h -1 m -2 . The indoor air temperature of the greenhouse varied from -4.3 to 42.4 °C while the outdoor temperature fluctuated between -13.8 to 10.6 °C. In addition, the differential temperature between modeled and measured data at climate conditions of snowy, rainy, cloudy and sunny days were 2.3, 0.2, 0.2, and 2.6 °C during daytime and -1.8, -2, 0.3 and 1 °C at nighttime, respectively. The obtained coefficient of determination (R 2 ) was 95.95% for measured and modeled data.