Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400-1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.
The objective of this study was to improve product quality of dehydrated fruits (apple, pear, papaya, mango) using combined drying techniques. This involved investigation of bioactivity, colour, and sensory assessment on colour of the dried products as well as the retention of the bio-active ingredients. The attributes of quality were compared in regard to the quality of dehydrated samples obtained from continuous heat pump (HP) drying technique. It was found that for apple, pear and mango the total colour change (ΔE) of samples dried using continuous heat pump (HP) or heat pump vacuum-microwave (HP/VM) methods was lower than of samples dried by other combined methods. However, for papaya, the lowest colour change exhibited by samples dried using hot air-cold air (HHC) method and the highest colour change was found for heat pump (HP) dehydrated samples. Sensory evaluation revealed that dehydrated pear with higher total colour change (ΔE) is more desirable because of its golden yellow appearance. In most cases the highest phenol content was found from fruits dried by HP/VM method. Judging from the quality findings on two important areas namely colour and bioactivity, it was found that combined drying method consisted of HP pre-drying followed by VM finish drying gave the best results for most dehydrated fruits studied in this work as the fruits contain first group of polyphenol compounds, which preferably requires low temperature followed by rapid drying strategy.
Solid-phase microextraction (SPME) is a solvent-less sample preparation method which combines sample preparation, isolation, concentration and enrichment into one step. In this study, multivariate strategy was used to determine the significance of the factors affecting the solid phase microextraction of pesticide residues (fenobucarb, diazinon, chlorothalonil and chlorpyrifos) using a randomised factorial design. The interactions and effects of temperature, time and salt addition on the efficiency of the extraction of the pesticide residues were evaluated using 2(3) factorial designs. The analytes were extracted with 100 μm PDMS fibres according to the factorial design matrix and desorbed into a gas chromatography-mass spectrometry detector. The developed method was applied for the analysis of apple samples and the limits of detection were between 0.01 and 0.2 μg kg(-)(1), which were lower than the MRLs for apples. The relative standard deviations (RSD) were between 0.1% and 13.37% with average recovery of 80-105%. The linearity ranges from 0.5-50 μg kg(-)(1) with correlation coefficient greater than 0.99.
A sensitive and selective gas chromatography with mass spectrometry method was developed for the simultaneous determination of three organophosphorus pesticides, namely, chlorpyrifos, malathion, and diazinon in three different food commodities (milk, apples, and drinking water) employing solid-phase extraction for sample pretreatment. Pesticide extraction from different sample matrices was carried out on Chromabond C18 cartridges using 3.0 mL of methanol and 3.0 mL of a mixture of dichloromethane/acetonitrile (1:1 v/v) as the eluting solvent. Analysis was carried out by gas chromatography coupled with mass spectrometry using selected-ion monitoring mode. Good linear relationships were obtained in the range of 0.1-50 μg/L for chlorpyrifos, and 0.05-50 μg/L for both malathion and diazinon pesticides. Good repeatability and recoveries were obtained in the range of 78.54-86.73% for three pesticides under the optimized experimental conditions. The limit of detection ranged from 0.02 to 0.03 μg/L, and the limit of quantification ranged from 0.05 to 0.1 μg/L for all three pesticides. Finally, the developed method was successfully applied for the determination of three targeted pesticides in milk, apples, and drinking water samples each in triplicate. No pesticide was found in apple and milk samples, but chlorpyrifos was found in one drinking water sample below the quantification level.
A fast and simple solvent microextraction technique using salting out-vortex-assisted liquid-liquid microextraction (salting out-VALLME) was developed for the extraction of furfurals (2-furfural (2-F), 3-furfural (3-F), 5-methylfurfural (5-MF) and 5-hydroxymethylfurfural (5-HMF)) and patulin (PAT) in fruit juice samples. The optimum extraction conditions for 5 mL sample were: extraction solvent, 1-hexanol; volume of extractant, 200 µL; vortex time, 45 s; salt addition, 20%. The simultaneous determination of the furfurals and PAT were investigated using high performance liquid chromatography coupled with diode array detector (HPLC-DAD). The separation was performed using ODS Hypersil C18 column (4.6 mm i.d × 250 mm, 5 μm) under gradient elution. The detection wavelengths used for all compounds were 280 nm except for 3-F (210 nm). The furfurals and PAT were successfully separated in less than 9 min. Good linearities (r(2)>0.99) were obtained within the range 1-5000 μg L(-1) for all compounds except for 3-F (10-5000 µg L(-1)) and PAT (0.5-100 μg L(-1)). The limits of detection (0.28-3.2 µg L(-1)) were estimated at S/N ratio of 3. The validated salting out-VALLME-HPLC method was applied for the analysis of furfurals and PAT in fruit juice samples (apple, mango and grape).
In this study, larvicidal activity of silver nanoparticles (AgNPs) synthesised using apple extract against fourth instar larvae of Aedes aegypti was determined. As a result, the AgNPs showed moderate larvicidal effects against Ae. aegypti larvae (LC50 = 15.76 ppm and LC90 = 27.7 ppm). In addition, comparison of larvicidal activity performance of AgNPs at high concentration prepared using two different methods showed that Ae. aegypti larvae was fully eliminated within the duration of 2.5 h. From X-ray diffraction, the AgNP crystallites were found to exhibit face centred cubic structure. The average size of these AgNPs as estimated by particle size distribution was in the range of 50-120 nm. The absorption maxima of the synthesised Ag showed characteristic Ag surface plasmon resonance peak. This green synthesis provides an economic, eco-friendly and clean synthesis route to Ag.
An herbal mixture composed of lemon, apple cider, garlic, ginger and honey as a polyphenol-rich mixture (PRM) has been reported to contain hypolipidemic activity on human subjects and hyperlipidemic rats. However, the therapeutic effects of PRM on metabolites are not clearly understood. Therefore, this study aimed to provide new information on the causal impact of PRM on the endogenous metabolites, pathways and serum biochemistry. Serum samples of hyperlipidemic rats treated with PRM were subjected to biochemistry (lipid and liver profile) and hydroxymethylglutaryl-CoA enzyme reductase (HMG-CoA reductase) analyses. In contrast, the urine samples were subjected to urine metabolomics using 1H NMR. The serum biochemistry revealed that PRM at 500 mg/kg (PRM-H) managed to lower the total cholesterol level and low-density lipoprotein (LDL-C) (p < 0.05) and reduce the HMG-CoA reductase activity. The pathway analysis from urine metabolomics reveals that PRM-H altered 17 pathways, with the TCA cycle having the highest impact (0.26). Results also showed the relationship between the serum biochemistry of LDL-C and HMG-CoA reductase and urine metabolites (trimethylamine-N-oxide, dimethylglycine, allantoin and succinate). The study's findings demonstrated the potential of PRM at 500 mg/kg as an anti-hyperlipidemic by altering the TCA cycle, inhibiting HMG-CoA reductase and lowering the LDL-C in high cholesterol rats.