Characterization of the Fruits and Seeds of <i>Alpinia Oxyphylla</i> Miq by High-Performance Liquid Chromatography (HPLC) and near-Infrared Spectroscopy (NIRS) with Partial Least-Squares (PLS) Regression

Li, Rong; Zeng, Canbiao; Li, Junni; Zhu, Kailian; Lin, Xinxin; Wen, Jingfeng; Guo, Huanjia; Weng, Wenfeng; Dong, Wang; Sheng-guo, JI

doi:10.1080/00032719.2020.1715996

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…Partial least squares (PLS), extreme learning machines (ELMs), backpropagation neural networks (BPNNs), support vector machine (SVM), and others, have been widely used in crop nutrient predictions. For example, Li et al [ 43 ] used PLS to establish 12 models of fruits and seeds for rapid analysis and quality assessment. Kira et al established a model for estimating the chlorophyll and carotenoid contents of three tree varieties based on BPNN [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

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Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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“…NIR can absorb light from bonding vibrations of C−H, O−H, and N−H. Inorganic compounds are not detected in the wavelength range of 780–2500 nm [ 42 ]. The original spectra are composed of a lot of noise and aberrations, so pretreatment is necessary.…”

Section: Resultsmentioning

confidence: 99%

“…The original spectra are composed of a lot of noise and aberrations, so pretreatment is necessary. Several pretreatment options that can be used are first derivative, second derivative, multiple scattering correction, wavelet transform, standard normal variate, Savitsky Golay, and Norris derivative [ 42 ]. Savitsky Golay is commonly used to reduce noise [ 43 ].…”

Section: Resultsmentioning

confidence: 99%

Nutritional Comparison of Sacha Inchi (Plukenetia volubilis) Residue with Edible Seeds and Nuts in Taiwan: A Chromatographic and Spectroscopic Study

Yanti

Agrawal

Saputri

et al. 2022

International Journal of Food Science

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Sacha inchi is a source of quality commercial oil in Taiwan. Oil extraction results in sacha inchi residue have not been utilized and not much investigated. Different edible seeds and nuts have different levels of nutrients. This study aims (a) to determine the oil, moisture, ash, protein, carbohydrate, type of fatty acid, resveratrol, and type of sugar in edible seeds and nuts, including sacha inchi residue, and (b) to determine the model to predict the five macronutrients using NIR spectroscopy. The samples used were candlenut, peanut, sesame, sunflower, sacha inchi residue, and black bean. Determination was conducted using NIR spectroscopy, NMR spectroscopy, LC-MS/MS, and HPLC-ELSD. NIR spectroscopy prediction results show that candlenut is rich in oil, and sacha inchi residue is rich in minerals, protein, and moisture. The correct prediction model for oil and moisture is principal component regression, while partial least squares are for ash, protein, and carbohydrates. NMR spectroscopy results showed that all samples were rich in polyunsaturated fatty acids. Sacha inchi residue is rich in omega 3. LC-MS/MS results showed that all samples contained resveratrol, and its highest level was found in sesame. HPLC-ELSD results showed eight types of sugars in the samples. High sucrose was found in sacha inchi residue, sunflower, sesame, and candlenut. The results are expected to provide information on nutrient levels in seeds and nuts to consumers and people who deal with nutrition. Also, results are expected to increase the economic value of sacha inchi residue as a source of diversification of food products in Taiwan.

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“…However, the above-mentioned bioassay method and physicochemical analysis method are still limited by many deficiencies in determining various food additives, such as high detection cost, long cycle, low sensitivity, poor reproducibility, complex sample preprocessing, and expensive equipment. Therefore, there is an increasing need to establish a rapid, accurate, and widely applicable method for the detection of ultra-micro food additives [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices

Chen

et al. 2021

Sensors

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It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (Rp) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10−6, and the limit of detection (LOD) is 2.3610 × 10−5 g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.

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Characterization of the Fruits and Seeds of Alpinia Oxyphylla Miq by High-Performance Liquid Chromatography (HPLC) and near-Infrared Spectroscopy (NIRS) with Partial Least-Squares (PLS) Regression

Cited by 11 publications

References 23 publications

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nutritional Comparison of Sacha Inchi (Plukenetia volubilis) Residue with Edible Seeds and Nuts in Taiwan: A Chromatographic and Spectroscopic Study

Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices

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