Near infrared spectroscopy as a rapid non-invasive tool for agricultural and industrial process management with special reference to avocado and sandalwood industries

Wedding, B.B.; Wright, Carole; Grauf, S.; White, R. D.; Gadek, Paul A.

doi:10.5004/dwt.2011.2723

Cited by 7 publications

(9 citation statements)

References 31 publications

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“…Based on other studies [8,10,30], the spectral regions that corresponded to the stretching and combinations of C-H 2 related to oil were identified as the wavelength bands located between 900 and 920 nm, at approximately 930 nm, 1200 nm, and 1750 nm, and between 2200 and 2400 nm. Our average spectra were similar to other published results [8,31]. However, this is the first study to show spectral differences between the fruit zones within the Hass avocado fruit via the NIRS technique.…”

Section: Number Of Nirs Scans Per Fruit Zone For Assessing the Dry Ma...supporting

confidence: 90%

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Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

Rodríguez,

Villamizar,

Londoño

et al. 2023

Plants

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Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R2: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R2: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.

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Section: Number Of Nirs Scans Per Fruit Zone For Assessing the Dry Ma...supporting

confidence: 90%

“…Finally, the VIP significative bands were 2306 and 2320 nm, which were associated with OC, as discussed later. The VIP >1 results found were related mainly to the observed spectral peaks exhibiting similarity to those found in the Hass avocado [8,11,19,24,31]. Subedi and K.B.…”

Section: Application Of Different Nirs Scans By Fruit In Maturity Mon...mentioning

confidence: 56%

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

Rodríguez,

Villamizar,

Londoño

et al. 2023

Plants

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“…In addition, numerous studies on the identification of tobacco varieties [9], tobacco parts [10], tobacco grades [11][12][13], aroma styles [14], and planting areas [15,16] using NIR spectroscopy techniques have also been carried out. More specifically, the distinguishing ability of NIR spectroscopy has been evaluated to determine the maturity levels of avocados [17][18][19][20], tomatoes [21,22], lychees [23], pomegranates [24], dates [25], table grapes [26], watermelons [27], cotton bolls [28], truffles [29], white teas [30], and peaches [31]. Despite the increasing number of applications of NIR spectroscopy in crop and fruit quality assessments, there are still only a few reports regarding the use of this technique to classify the maturity levels of fresh tobacco leaves.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Chen

Bin

Zou

et al. 2021

Journal of Analytical Methods in Chemistry

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The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

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“…In addition, NIR results are accurate compared to other analytical techniques. NIR technology has been widely used to detect numerous nutrients and toxic elements in agricultural foods during agro-industrial production and to ensure product safety [27,28]. NIR could also be used in the petrochemical industry for the determination of hydrocarbons, alcohols, ketones and nitrile compounds in organic solvents [29].…”

Section: Discussionmentioning

confidence: 99%

Genome-wide association analysis uncovers candidate genes for forage quality traits in Brassica napus stems

Wei¹,

Liu²,

Xu³

et al. 2020

Preprint

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Background Brassica napus (rapeseed) is an important oilseed crop and its leaves and stems can also be used as animal feed. Lignocellulose content is closely related to the nutritional quality and palatability of animal feed. However, quantitative trait loci (QTLs) associated with acid detergent fiber (ADF) and neutral detergent fiber (NDF) contents in rapeseed stems have not yet been mapped. Results In this study, we used 494 B. napus accessions to perform genome-wide association studies (GWAS) of ADF and NDF contents. Ninety-two single-nucleotide polymorphisms (SNP) and 35 simple-sequence repeat (SSR) loci were significantly correlated with ADF and NDF contents, respectively, and six genetic loci associated with ADF and NDF contents were detected using both types of markers. We identified three candidate genes on chromosome A05 related to ADF content, including genes encoding chitinase-like protein 2 (CTL2) and two trichome birefringence-like 41 s (TBL41s). Seven genes on chromosomes A03 and A04 were related to NDF content, including genes encoding glycosyl hydrolase (GH), reversibly glycosylated polypeptide 1 (RGP), irregular xylem 12 (IRX12), trichome birefringence-like 34 (TBL34), galacturonosyltransferase 7 (GAUT7), cytokinesis defective 1 (CYT1), and LOB domain-containing protein 15 (LBD15). These candidate genes encode factors that likely participate in secondary cell wall formation and lignocellulose biosynthesis. Conclusions These findings lay the foundation for identifying genes related to forage quality traits and improving the efficiency of forage utilization in rapeseed, which will be beneficial for breeding new varieties for high-quality forage with low lignocellulose content.

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Near infrared spectroscopy as a rapid non-invasive tool for agricultural and industrial process management with special reference to avocado and sandalwood industries

Cited by 7 publications

References 31 publications

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Genome-wide association analysis uncovers candidate genes for forage quality traits in Brassica napus stems

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