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Assessment of Intact Macadamia Nut Internal Defects Using Near-Infrared Spectroscopy
... Best-fit classification models (100%) were developed using all wavelengths and spectra from images of face-up kernels and were marginally more accurate than models developed using images of kernels in face-down (98%), or pooled image (98%) orientations. The best-fit model using VNIR face-down images was more accurate than another study using the NIR region (980-1680 nm) that reported 88.2% accuracy [60]. This may be attributed to the hyperspectral images in this study collecting both spectral and spatial data and, therefore, allowing inspection of a greater kernel surface area in comparison with the NIR point method. ...
Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling.
... For example, color adulterant in red chili [8], fraud detection in meat [9], and adulterated almond powder with apricots and peanuts [10]. Further, hyperspectral imaging demonstrated high potential for qualitative and quantitative analysis of agriculture crops, for instance, the determination of chemical contents of water and tuber flour [11], prediction of anthocyanins in black rice [12], assessments of internal defects in macadamia [13] and quality analysis of stored bell peppers [14]. ...
The emergence of paraffin-coated rice in China, aimed at enhancing its market appeal and achieving a translucent appearance, has given rise to a significant global food safety concern. This situation poses substantial health risks to consumers. Hyperspectral analysis, recognized as a powerful and nondestructive technique for assessing food quality and safety, offers a potential solution. This study conducted a comprehensive investigation using Visible-Near Infrared (VIS-NIR) hyperspectral imaging systems operating within the 400-1000 nm range to identify paraffin-contaminated rice. Various rice varieties from diverse regions were obtained and intentionally tainted with varying levels of paraffin. Imaged samples were further preprocessed for spectral data extraction from individual rice seeds’ regions of interest (ROI). The dataset encompassed 3000 spectral records obtained from both non-contaminated and contaminated samples. The obtained spectral data were employed to develop partial least squares discriminant analysis (PLS-DA) and principal component linear discriminant analysis. The primary goal was to discriminate between contaminated and non-contaminated rice samples effectively. Notably, the results indicated that PLS-DA consistently achieved an accuracy exceeding 94% across various preprocessing techniques. Overall, this study showcased the potential of combining hyperspectral imaging with chemometrics to detect paraffin-contaminated rice seeds, providing a valuable contribution to food safety assessment in the industry.
... In Fig. 2(b) a district spectral differences between the good and bad macadamia nut can be observed; probably due to chemical associated with internal defects, such as insect damage, cracks, mold, and so on. In previous studies, absorption bands between 1350-1400 nm were related to the presence of glucose, sucrose, and fructose in the nuts, while bad nuts (mainly immature kernels) have higher sucrose and reducing sugar contents than the fully mature kernels (de Carvalho et al., 2019;Rahman et al., 2020). Also, peaks between 1100-1400 nm were related to the combination and overtone vibrations of functional groups, such as hydrogen (C-H, N-H, and O-H) bonds, which can be used to distinguish internal tissue damage of the nut (Duduzile Buthelezi et al., 2019). ...
As production and demand for high quality Macadamia nuts rise worldwide, rapid and accurate in-situ methods for monitoring internal nut quality will need to be developed. Therefore, purpose of this study was to determine the quality (internal nut defects and oil content) of intact macadamia nuts by combining near-infrared spectroscopy with variable selection algorithms and calibration models. Transmission spectra in the ranges of 1,000-1,650 nm from a total of 345 macadamia nuts were acquired. Partial least square-discriminant analysis (PLS-DA) and partial least square-regression (PLS-R) models with three different spectral preprocessing techniques were then evaluated using the full spectra to classify nut defects and predict oil content. A Savitzky-Golay (S-G) first derivatives preprocessing technique was selected as the best one. Then, competitive adaptive reweighted sampling (CARS), random frog (RF), and variable importance projection (VIP) algorithms were used to select effective wavelengths (EWs) to build simpler and more robust models for classification of intact macadamia nut defects and prediction of oil content. The PLS-DA-CARS with S-G first derivatives preprocessing based model provided the best nut defect classification with a 91.4% accuracy, whilst a PLS-R-CARS with S-G second derivatives preprocessing model for oil content prediction had a coefficient of determination (R²pred) of 0.88 and standard error of prediction (SEP) of 1.15%.
The pungency level of green peppers is dependent on the amounts of capsaicin and dihydrocapsaicin they contain. This study was conducted to develop a non-destructive method for the prediction and mapping of the capsaicin and dihydrocapsaicin contents in green pepper. Hyperspectral images of 200 total green peppers of three varieties were acquired in the wavelength range of 1000–1600 nm, from which the mean spectra of each pepper variety were extracted. The reference capsaicin and dihydrocapsaicin contents of the samples were measured by high-performance liquid chromatography. Quantitative calibration models were built using partial least squares (PLS) regression with different spectral preprocessing techniques; the best performance was found by normalizing the preprocessed spectra with correlation coefficients (rpred ) of 0.86 and 0.59, which showed the standard errors of prediction (SEPs) of 0.09 and 0.03 mg/g for capsaicin and dihydrocapsaicin, respectively. Seventeen and 16 optimal wavebands were selected using the successive projections algorithm; rpred of 0.88 and 0.68 and SEPs of 0.084 and 0.027 mg/g were obtained for capsaicin and dihydrocapsaicin, respectively, from the newly developed PLS calibration models using these optimal wavebands. The successive projections algorithm (SPA)-PLS model was used to map the capsaicin and dihydrocapsaicin contents of the green peppers. These maps provided detailed information on the pungency levels of the tested green peppers. The results of this study indicated that hyperspectral imaging is useful for the rapid and non-destructive evaluation of the pungency of green peppers.
Macadamia nut industry is increasingly gaining more space in the food market and the success of the industry and the quality is largely due to the selection of cultivars through macadamia nut breeding programs. Thus, the objective of this study was to investigate the feasibility NIRS coupled to chemometric classification methods, to build a rapid and non-invasive analytical procedure to classify different macadamia cultivars based on intact nuts. Intact nuts of five different macadamia cultivars (HAES 246, IAC 4-20, IAC 2-23, IAC 5-10, and IAC 8-17) were harvested in 2017. Two NIR reflectance spectra were collected per nut and the mean spectra was used to chemometrics analysis. Principal component analysis-linear discriminant analysis (PCA-LDA) and genetic algorithm-linear discriminant analysis (GA-LDA) were used to develop the classifications models. The GA-LDA approach resulted in accuracy higher than 94.44%, with spectra preprocessed with Savitzky-Golay smoothing. Thus, this approach can be implemented in the macadamia industry, allowing the selection of cultivars based on intact nuts. However, is recommended more experimentation to include more data variability in order to increase the classification accuracy to 100%.
The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000–1550 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky–Golay (S–G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model. These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes.
Ultraviolet–visible (UV-VIS) spectral properties of eye fluid were used to classify Japanese dace fish into fresh or spoiled groups by support vector machine (SVM), linear discriminant analysis (LDA) based on principal component analysis (PCA) scores and using a soft independent modeling of class analogy (SIMCA) classification technique. These models were then evaluated in terms of their sensitivity, specificity and overall accuracy. The UV-VIS absorbance spectra (250–600 nm) of eye fluid from 168 fishes and the K value of fish flesh were measured at 3 h intervals for 36 h. In the SVM model, the sensitivity and specificity for fresh fish was 100%, whereas in the LDA and SIMCA models it was 100% and 90%, and 80% and 90% respectively. For the spoiled group fish the sensitivity and specificity result was 100% for the SVM model, while in the LDA and SIMCA models it was 90% and 100%, and 90% and 80% respectively. The overall classification accuracy was 100%, 93% and 87% for the SVM, LDA and SIMCA models respectively. These results, particularly for the SVM model, indicate that the spectral absorbance of fish eye fluid in the UV-VIS region can accurately classify fish into fresh and spoiled groups.
Spectral data were collected of intact and ground kernels using 3 instruments (using Si-PbS, Si, and InGaAs detectors), operating over different areas of the spectrum (between 400 and 2500 nm) and employing transmittance, interactance, and reflectance sample presentation strategies. Kernels were assessed on the basis of oil and water content, and with respect to the defect categories of insect damage, rancidity, discoloration, mould growth, germination, and decomposition. Predictive model performance statistics for oil content models were acceptable on all instruments (R² > 0.98; RMSECV < 2.5%, which is similar to reference analysis error), although that for the instrument employing reflectance optics was inferior to models developed for the instruments employing transmission optics. The spectral positions for calibration coefficients were consistent with absorbance due to the third overtones of CH2 stretching. Calibration models for moisture content in ground samples were acceptable on all instruments (R² > 0.97; RMSECV < 0.2%), whereas calibration models for intact kernels were relatively poor. Calibration coefficients were more highly weighted around 1360, 740 and 840 nm, consistent with absorbance due to overtones of O-H stretching and combination. Intact kernels with brown centres or rancidity could be discriminated from each other and from sound kernels using principal component analysis. Part kernels affected by insect damage, discoloration, mould growth, germination, and decomposition could be discriminated from sound kernels. However, discrimination among these defect categories was not distinct and could not be validated on an independent set.
It is concluded that there is good potential for a low cost Si photodiode array instrument to be employed to identify some quality defects of intact macadamia kernels and to quantify oil and moisture content of kernels in the process laboratory and for oil content in-line. Further work is required to examine the robustness of predictive models across different populations, including growing districts, cultivars and times of harvest.
This paper intends to review the basic theory of Near Infrared (NIR) Spectroscopy and its applications in the field of Analytical Science. It is addressed to the reader who does not have a profound knowledge of vibrational spectroscopy but wants to be introduced to the analytical potentialities of this fascinating technique and, at same time, be conscious of its limitations. Essential theory background, an outline of modern instrument design, practical aspects, and applications in a number of different fields are presented. This work does not intend to supply an intensive bibliography but refers to the most recent, significant and representative material found in the technical literature. Because this paper has been produced as consequence of the First Workshop on Near Infrared Spectroscopy, whose venue was Campinas - Brazil, as a pre-conference activity of the XI National Meeting on Analytical Chemistry (ENQA), it also depicts the state of the art of NIR spectroscopy in Brazil, pointing out the current achievements and the need to take the technology to a level consistent with this country's economical activities.
Moisture content (MC), oil content (OC), fatty acid composition and rancidity are considered as major determinants of quality of nuts. These parameters are destructively quantified from a batch of representative samples used to estimate quality of nuts of an entire orchard. Although destructive techniques are helpful, they involve extensive sample preparation and solvent extractions, are slow, expensive and obtained results specifically reflect the properties of the evaluated produce. Recently, non-invasive analytical methods and instruments for evaluating quality of various produce have become popular with researchers putting more effort in developing them. Non-destructive methods are an alternative to traditional methods for inspection of internal quality parameters because they are fast, simple and cost-effective. In this review, invasive and non-invasive analytical methods and instruments for evaluating MC, OC, fatty acid composition and rancidity in different nuts are discussed. This paper also reviews the implementation of visible to near infrared spectroscopy, nuclear magnetic resonance and X-ray computed tomography on nuts for evaluation of quality attributes. Technical challenges and future possibilities for commercial use of these non-invasive methods for quality evaluation of nuts are presented.
en The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (Rp²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, Rp² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.
Practical Applications
pt Near‐infrared spectroscopy (FT‐NIR) was used to evaluate the quality of macadamia nuts, specially to access one of the major shelled macadamia nut defects (insect damage) and the onset of lipid oxidation in unshelled macadamia nuts. Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict peroxide value (PV) and acidity index (AI) in unshelled macadamia nuts and can be used by the food industry in quality control processes.
Macadamia is a rainforest tree indigenous to Australia that is grown commercially for its edible nuts. This chapter discusses quality and the preharvest and postharvest practices that impact on macadamia quality. The chapter first reviews botany, the macadamia industry, fruit development, and measures of quality suchas oil content, quality defects, appearance and rancidity. Preharvest impacts on quality such as cultivar, site, crop management and pest and diseases are considered. Key postharvest processes that are crucial for macadamia quality such as harvest methods, drying, postharvest handling and factory processing are also reviewed.