Determination of terpenoid contents of aromatic plants using NIRS

Ercioglu, Elif; Velioğlu, Hasan Murat; Boyaci, İsmail Hakkı

doi:10.1016/j.talanta.2017.10.017

Cited by 32 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14 Moreover, the amount of biomass required to scan fresh samples would be high because of the small shape of conifer foliage. Fresh leaves can also be transformed into pellets, 13 but this processing does not necessarily reduce sample manipulations.…”

Section: Resultsmentioning

confidence: 99%

“…Measuring terpene concentration and composition is a costly and lengthy procedure, usually requiring the extraction of the oil content of the plants, followed by gas chromatography analyses. 13,14 Near infrared (NIR) spectroscopy could address these limitations if the NIR spectra of plant tissues can be correlated with their terpene content. Successful calibration of total terpene content or specific terpenes content with NIR spectra has been reported with oil-based samples.…”

Section: Introductionmentioning

confidence: 99%

“…Successful calibration of total terpene content or specific terpenes content with NIR spectra has been reported with oil-based samples. 13,15,16 Oil extraction, however, can require a relatively large amount of biomass (e.g. 100 g of dried, ground plant sample), 13 a luxury not always available in experimental studies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting terpene content in dried conifer shoots using near infrared spectroscopy

Champagne

Bonin

Royo

et al. 2020

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Terpenes are phytochemicals found in multiple plant genera, especially aromatic herbs and conifers. Terpene content quantification is costly and complex, requiring the extraction of oil content and gas chromatography analyses. Near infrared (NIR) spectroscopy could provide an alternative quantitative method, especially if calibration can be developed with the spectra of dried plant material, which are easier and faster to acquire than oil-based spectra. Here, multispecies NIR spectroscopy calibrations were developed for total terpene content (mono- and sesquiterpenes) and for specific terpenes (α-pinene, β-pinene and myrcene) with five conifers species ( Picea glauca, Picea rubens, Pinus resinosa, Pinus strobus and Thuja occidentalis). The terpene content of fresh shoot samples was quantified with gas chromatography. The NIR spectra were measured on freeze-dried samples (n = 137). Using a subset of the samples, modified partial least squares regressions of total terpene and the three individual terpenes content were generated as a functions of the NIR spectra. The standard errors of the internal cross-validations (values between 0.25 and 2.28) and the ratio of prediction to deviation ratios (RPD values between 2.20 and 2.38) indicate that all calibrations have similar accuracy. The independent validations, however, suggest that the calibrations for total terpene and α-pinene content are more accurate (respective coefficient of determination: r2 = 0.85 and 0.82). In contrast, calibrations for β-pinene and myrcene had a low accuracy (respectively: r2 = 0.62 and 0.08), potentially because of the low concentration of these terpenes in the species studied. The calibration model fits (i.e., r2) are comparable to previously published calibration using the spectra of dried shoot samples and demonstrate the potential of this method for terpenes in conifer samples. The calibration method used could be useful in several other domains (e.g. seedling breeding program, industrial), because of the wide distribution of terpenes and especially of pinenes.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting terpene content in dried conifer shoots using near infrared spectroscopy

Champagne

Bonin

Royo

et al. 2020

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

show abstract

“…23 PCA of the spectrum can reduce the dimensionality of the original data space by using a smaller and more efficient abstract space of latent variables to eliminate the correlation and irrelevant information existing in the original spectral data, and extract useful information. 24,25 Figure 4 shows the scores calculated on the first two principal components (PC-1 and PC-2), which can uncover the ability of NIR spectroscopy to recognize the different types of samples. It was obvious from Figure 4 that the samples were divided into four groups clearly.…”

Section: Resultsmentioning

confidence: 99%

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

She

Li²,

Hou³

et al. 2019

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

The solidification point is a key quality parameter for 2,4,6-trinitrotoluene (TNT). The traditional solidification point measurement method of TNT is complicated, dangerous, not environmentally friendly and time-consuming. Near infrared spectroscopy (NIR) analysis technology has been applied successfully in the chemical, petroleum, food, and agriculture sectors owing to its characteristics of fast analysis, no damage to the sample and online application. The purpose of this study was to study near infrared spectroscopy combined with chemometric methods to develop a fast and accurate quantitative analysis method for the solidification point of TNT. The model constructed using PLS regression was successful in predicting the solidification point of TNT ([Formula: see text] = 0.999, RMSECV = 0.19, RPDCa = 33.5, [Formula: see text] = 0.19, [Formula: see text] = 0.999). Principal component analysis shows that the model could identify samples from different reactors. The results clearly demonstrate that the solidification point can be measured in a short time by NIR spectroscopy without any pretreatment for the sample and skilled laboratory personnel.

show abstract

“…Therefore, the sample is commonly low for moisture content measurements at maize grain-filling stage. Near-infrared spectroscopy (NIRS) combined with chemometrics has become a popular technique for quantitative analysis [7][8][9] , quality detection [10][11][12] , and identification of seed varieties [13] . As a result of the changes in the actual application requirements and the continuous advancement of technology, NIRS has gradually become a monitoring technique in areas such as food, medicine, environment [14,15] , materials [16,17] , and crop growth [18,19] .…”

Section: Introductionmentioning

confidence: 99%

Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size

Wang¹,

Yang²,

Song³

et al. 2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The change in the maize moisture content during different growth stages is an important indicator to evaluate the growth status of maize. In particular, the moisture content during the grain-filling stage reflects the grain quality and maturity and it can also be used as an important indicator for breeding and seed selection. At present, the drying method is usually used to calculate the moisture content and the dehydration rate at the grain-filling stage, however, it requires large sample size and long test time. In order to monitor the change in the moisture content at the maize grain-filling stage using small sample set, the Bootstrap re-sampling strategy-sample set partitioning based on joint x-y distances-partial least squares (Bootstrap-SPXY-PLS) moisture content monitoring model and near-infrared spectroscopy for small sample sizes of 10, 20, and 50 were used. To improve the prediction accuracy of the model, the optimal number of factors of the model was determined and the comprehensive evaluation thresholds RVP (coefficient of determination (R 2), the root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP)) was proposed for sub-model screening. The model exhibited a good performance for predicting the moisture content of the maize grain at the filling stage for small sample set. For the sample sizes of 20 and 50, the R 2 values were greater than 0.99. The average deviations of the predicted and reference values of the model were 0.1078%, 0.057%, and 0.0918%, respectively. Therefore, the model was effective for monitoring the moisture content at the grain-filling stage for a small sample size. The method is also suitable for the quantitative analysis of different concentrations using near-infrared spectroscopy and small sample size.

show abstract

Determination of terpenoid contents of aromatic plants using NIRS

Cited by 32 publications

References 13 publications

Predicting terpene content in dried conifer shoots using near infrared spectroscopy

Predicting terpene content in dried conifer shoots using near infrared spectroscopy

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size

Contact Info

Product

Resources

About