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Conduct disorder is one of the most serious and complex complications in school-aged children. This study was conducted with the aim of comparing the effectiveness of Good Enough Parenting training with schema therapy approach and positive parenting program training on the parent-child conflict in mothers of children with conduct disorders. The method of the current research was a quasi-experimental one with pretest-post-test method plus a control group and a follow-up stage. The population included all mothers of students with conduct problems in the second grade of elementary school in Tehran in the academic year of 2020-2021. Out of this population, after the implementation of the Sprafkin test (the fourth edition) a sample of 60 participants (three groups of 20 people from mothers of children, including two experimental groups and one control group) were selected using the available sampling. They were given Sprafkin children's behavioral syndrome questionnaire and the parentchild conflict questionnaire. The results were analyzed using repeated measurements analysis of variance. The results indicated that both interventions led to a significant reduction in parent-child conflicts. Also, there was no significant difference between their effectiveness. In general, the results revealed that Good Enough Parenting training with schema therapy approach and positive parenting program training has a significant effect on parent-child conflict. The results of this study can be benefited in psychology, especially in the field of parenting. [ABSTRACT FROM AUTHOR]

In this study, a portable optical instrument was designed and developed for real-time nondestructive determination of fruit ripeness. A prototype of the instrument using inexpensive parts was developed and calibrated for apple ripeness detection based on moisture content, soluble solids content, pH, and firmness. An array of six optical point sources (light-emitting diodes (LEDs) that emit light at 520-, 670-, 920-, 970-, 1050-, and 1320-nm wavelengths) were selected using visible/near infrarred (Vis/NIR) spectral analysis on apple samples. The instrument was equipped with optical fibers, optical sensors, and an Arduino board for sending and receiving the waves and processing the received signals, as well as controlling the operation of the instrument. The decision tree method was utilized to create a classification model for training the instrument to classify ripe and unripe apples. Test results showed acceptable performance of the developed instrument for apple ripeness estimation and proved useful in classifying fruits into ripe and unripe groups based on their internal quality. [ABSTRACT FROM AUTHOR]

Visible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes ('Agria' and 'Clone 397009–8') stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R2C and R2CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R2C = 0.99, R2CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in 'Clone' genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R2C = 0.93, R2CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored 'Clone' genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes. [ABSTRACT FROM AUTHOR]

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Absorption and scattering properties of product change as moisture content is reduced, but it has not been investigated how these changes are correlated. This study was aimed to measure and test the feasibility of using optical properties in predicting the moisture content of sliced apple samples during hot air drying. In this investigation, the noninvasive backscattering laser imaging technique at three wavelengths (650, 780, and 880 nm) and Farrell’s diffusion theory were used to determine absorption and reduced scattering coefficients. Artificial neural network model was applied to correlate the optical coefficients and moisture content of samples. The highest correlation between above-mentioned parameters was found at 780 nm. The best moisture content prediction result was obtained when absorption and reduced scattering coefficients were combined at three wavelengths withRp = 0.984. The results suggested that this method can be effectively used to predict the moisture content and control the drying process. [ABSTRACT FROM PUBLISHER]

In this research, an optical system based on fibre optic Vis/NIR spectroscopy combined with chemometrics methods and software as a graphical user interface (GUI) was developed and presented for fast and non-destructive detection and determination of pesticide residues in agricultural products (a case study on diazinon in intact cucumbers). Vis/NIR spectra of cucumber samples without and with different concentrations of diazinon residue were analyzed at the range of 450–1000 nm. Partial least squares (PLS) regression models were developed based on chemical reference measurements and the spectral information of the samples after performing different pre-processing methods. Moreover, partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the maximum residue limits (MRL) as safe and unsafe samples, respectively. Finally, user-friendly software as a GUI was created based on the best PLS and PLS-DA models developed for prediction of diazinon contents in the samples and for classification of intact cucumbers by the absence/presence of diazinon residues, respectively. Evaluation of the system and software designed based on the best developed PLS and PLS-DA models indicated good performance for measuring and detection of diazinon residue in cucumbers. It was concluded that the designed system and software based on Vis/NIR spectroscopy combined with chemometrics methods can be utilized for fast and non-destructive safety control of intact cucumbers by the absence/presence of diazinon residues. It can also be generalized for detection of other pesticide residues in agricultural products if developing their appropriate models is feasible. [ABSTRACT FROM AUTHOR]

The feasibility of using visible/near-infrared (Vis/NIR) spectroscopy was assessed for non-destructive detection of diazinon residues in intact cucumbers. Vis/NIR spectra of diazinon solution and cucumber samples without and with different concentrations of diazinon residue were analysed at the range of 450–1000 nm. Partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the MRL as safe and unsafe samples, respectively. The best model was obtained using a first-derivative method with the lowest standard error of cross-validation (SECV = 0.366). Moreover, total percentages of correctly classified samples in calibration and prediction sets were 97.5% and 92.31%, respectively. It was concluded that Vis/NIR spectroscopy could be an appropriate, fast and non-destructive technology for safety control of intact cucumbers by the absence/presence of diazinon residues. [ABSTRACT FROM AUTHOR]

Reflectance Visible/near-infrared spectroscopy was used to analyze the effect of peel on the non-destructive soluble solids content prediction of oranges. The spectra were interpreted and partial least squares model was developed for both intact and peeled oranges at two wavelength ranges of 450–1000 and 700–1000 nm. Results showed that the wavelengths of the main absorbance peaks on the spectra of the intact and peeled samples were the same (p> 0.05). Moreover, the developed models yielded a good prediction accuracy of soluble solids content (2.5 < standard deviation ratio < 3) for intact oranges as well as the peeled fruits at the spectral ranges of 450–1000 and 700–1000 nm (p> 0.05). It was also noted that the effect of peel chemical composition can be ignored in non-destructive spectroscopy of intact citrus for determination of soluble solids content. [ABSTRACT FROM PUBLISHER]

The feasibility of utilizing near-infrared (NIR) spectroscopy has been recently assessed for rapid and non-destructive detection of internal insect infestation in some fruits. Based on the findings, this technology can be used for on/in-line inspection of the fruits in terms of insect infestation if suitable instrument is selected for accurate spectral measurements and system development. The spectral range and optical measurement mode are two of the most important factors which can affect the accuracy of the spectral measurements and detection models. The aim of this study is meta-analysis of these factors' effects on the ability of NIR-based spectroscopy for non-destructive detection of hidden insect infestation in fruits. Eight studies (65 observations) were extracted based on the criteria of this study. Overall, utilizing NIR-based spectroscopy led to 13.98% error (95% CI = 10.69–17.27%) for non-destructive detection of hidden insect infestation in fruits. Spectral ranges of Vis/SWNIR (above 300 up to 1100 nm), NIR (above 780 up to 2500 nm), and Vis/NIR (above 300 up to 2500 nm) showed errors of 21.71% (95% CI = 16.56–26.86%), 13.30% (95% CI = 5.24–21.36%), and 13.65% (95% CI = 5.9–21.4%), respectively. It was noted that wavelengths above 1100 nm (NIR region) are more useful to detect insect infestation in fruit. Results also indicated that optical measurement modes of interactance, reflectance, and transmittance had errors of 6.66% (95% CI = 4.18–9.14%), 15.73% (95% CI = 10.99–20.47%), and 16.04% (95% CI = 7.26–24.82%), respectively. This meta-analysis suggests that utilizing interactance mode for spectra measurement in NIR-based spectroscopy can increase the accuracy of discrimination of insect infested fruits especially when the spectral range of the spectrometer is Vis/SWNIR. Moreover, it should be selected a spectrometer with the wavelength range of NIR or Vis/NIR when using reflectance or transmittance mode is necessary for developing an in/on-line system to detect insect infestation in fruits. [ABSTRACT FROM AUTHOR]

Highlights • Wavelet transform was used as pre-processing tool in Vis/NIR spectral analysis. • Developed models showed better regression performance than previous studies. • Wavelet transform showed acceptable performance for denoising of spectral data. Abstract Quality assessment of fruits and vegetables is important for all involved in production, processing, trading and even consumption of the products. This study was conducted for non-destructive estimation of moisture content (MC), soluble solids content (SSC), pH and firmness of Gala apple samples using near-infrared spectroscopy in the 350–2500 nm range by application of wavelet transform for pre-processing of raw spectral data. Wavelet transform was combined with other usual pre-processing functions widely used for constructing PLS regression models. The models' predictive capability was evaluated using correlation coefficient of determination (R2), root mean square error of estimation (RMSE) and correlation coefficient (R). The best calibration and validation results were obtained for MC with 8 factors, R2 C = 0.90, RMSEC = 0.0042, R2 CV = 0.88, and RMSECV = 0.0047. The PLS regression parameters for SSC with 9 model factors, were R2 C = 0.90, RMSEC = 0.37, R2 CV = 0.86, and RMSECV = 0.45. The calibration and validation model parameters for pH and firmness with 10 model factors were R2 C = 0.87, RMSEC = 0.04, R2 CV = 0.84, RMSECV = 0.05, and R2 C = 0.76, RMSEC = 2.23, R2 CV = 0.0.59, and RMSECV = 3.04, respectively. In the prediction set, the model that was developed for moisture content had RMSEP = 0.009, R2p = 0.6. The R2p, and RMSEP for SSC were, 0.87 and 0.55, respectively. The model prediction parameters for pH were 0.72 and 0.06 for, R2p and RMSEP, respectively. Finally, predicted firmness of apple samples were acceptable (R2p = 0.65 and RMSEP = 3.86). Because of noise removal ability, application of wavelet transform for pre-processing on spectral data led to accurate, simple and fast development of PLS regression models. [ABSTRACT FROM AUTHOR]

The quality of agricultural products is of high importance in terms of consumer interest, determining market acceptance, and thus, directly affects storage and post-harvest processing operations. Quality measurement of fruits, vegetables and food products is at the center of attention by the food industry. Non-destructive measurements of quality parameters have been conducted on many agricultural products and have proved to be rapid and accurate in estimating the quality factors involved. While, non-destructive methods are very useful for quality testing, the recent interest by consumers, researchers, and the food industry in the application of new portable and/or handheld non-destructive devices for quality assessment of agricultural products has added a new dimension to the issue. Because these devices are small-sized, low-cost, low-weight, and easy to use, they can be utilized by farmers, quality inspectors, and even consumers. Thus, researchers have focused on developing portable non-destructive devices for a variety of food items. This review, examines the latest reports on the design and development of dedicated non-destructive portable and/or handheld devices for quality monitoring of agricultural products. [ABSTRACT FROM AUTHOR]

• A numerical example is provided for using non-destructive spectroscopy of safety control. • Monitoring system built on a spectral analysis of different microbial population assessment. • The suggested method of using GA algorithm is proposed for adaptive sensor development. Microbial contamination of vegetables is always known as a major food safety challenge. Persian leek is one of the most widely used herbs. Detection of Escherichia coli (E. coli ATCC 8739) in vegetables is very important issue for post-harvest management. Non-destructive assessment of E. coli ATCC 8739 contamination of leek as a main aim of this study were investigated using visibe/near-infrared spectroscopy combined with three variable selection approaches and partial least squares discriminant analysis as a promising chemometrics tool: Genetic Algorithm-PLSDA, interval PLS algorithm-DA and variable influence on projection scores-PLSDA. Reflectance spectra in interactance mode were measured on different treatments of E. coli ATCC 8739 solution (0.1, 0.2 and 0.3 mL) on leek samples in the spectral range of 350–1100 nm. The spectra (R) were captured at three different positions of sample by random in triplicate. The calibration and internal validation sets were created with 150 (75 %) and 50 (25 %) leek samples, respectively. In order to prevent incorrect results due to over-fitting, the final developed model has been deeply validated with external validation set of 50 samples. Five key features selected by genetic algorithm showed high performance of E. coli ATCC 8739 detection with 100 %, 98 % and 0.8 of sensitivity, specificity and classification error, respectively. Besides, a high prediction ability (>95 % correct classification in internal validation set and >89 % in external validation set) were achieved between 490, 500, 570, 740, and 970 nm spectral distances and the presence of E. coli ATCC 8739 contamination. In general, The Vis/NIR spectroscopy method for non-destructive detection of E. coli ATCC 8739 contamination on Persian leek in food safety application is feasible and practical. We further demonstrate the capability of genetic algorithm to detect the bacterial cells in the samples at the early growth stage. [ABSTRACT FROM AUTHOR]

• Safety assessment of lettuce based on E. coli using the Vis/NIR spectra is possible. • SIMCA & SVM can be more reliable than PCA & HCA to distinguish unsafe samples. • Microbial population amount cause a complex process in lettuce. • Chemical component of lettuces with E. coli concentration have a non-linear relationship. • PLSDA provides a robust qualitative model accurately classified lettuce samples. Escherichia coli is a main cause of microbial contamination in lettuce. Detection of microbial contamination is the essential key to ensure the consumption of fresh lettuce. In this investigation, the potential of Vis/NIR spectroscopy system with chemometrics analysis was probed for detecting different microbial loads (0.1, 0.2 and 0.3 ml concentration of E. coli solution) on the lettuce samples in the wavelength range of 350–1100 nm. Five different chemometrics analyses consist of soft independent modeling of class analogies (SIMCA), support vector machine (SVM), Partial least Squares Discriminant Analysis (PLS-DA) (supervised techniques), principal component analysis (PCA) and hierarchical cluster analysis (HCA) (unsupervised techniques) were used. The results proved that HCA were correctly clustered unsafe samples with 0.2 ml and 0.3 ml microbial contamination. However, some overlapping was observed between safe and unsafe samples with 0.1 ml microbial contamination. Vis/NIR spectral data with pattern recognition methods (SIMCA and SVM) can be obtained acceptable degree of classification (87.1% and 89.39% accuracy, respectively) between safe and unsafe samples. Compare with 5 different methods, the best model was recommended by PLS-DA using standard normal variation (SNV) + second derivate (D 2) pre-processing methods in 6 optimal selected wavelengths (520, 670, 700, 750, 900, 970 nm) with the minimum standard deviation of cross-validation (SECV = 0.176). Besides, a good correlation (r c = 0.989) between Vis/NIR spectral data and E. coli contamination proved the possibility of using Vis/NIR spectroscopy system to detection and evaluation of microbial contamination in lettuce. [ABSTRACT FROM AUTHOR]

Initial low cost of pesticides and lack of knowledge on the part of agricultural producers has led to an overuse of pesticides, with dangerous consequences. On the other hand, today, pesticides have an important role in the increment of agriculture products. Therefore, in recent years, the major aims of agricultural, environmental and even the food industry researchers have been to reduce the usage of chemical materials in agricultural applications. To achieve this, variable rate application in sprayers using different technologies such as machine vision, ultrasonic sensors, spectrometry, laser scanning, as well as pest identification and classification, have been applied in recent studies. This paper reviews these works reported in the 21 century. In addition, the best result is suggested in the conclusion section for future studies. [ABSTRACT FROM AUTHOR]