Identification and precise mapping of PmHSM, a novel recessive powdery mildew resistance allele from wheat landrace Heshangmai.

Common wheat (Triticum aestivum L.) is the world's primary food crop, and ensuring its safe production is of utmost importance for global peace and human development. However, the continuous threat of fungal diseases, including Fusarium head scab, rusts, sharp eyespot, and powdery mildew (PM), poses a significant challenge to production. PM caused by Blumeria graminis f. sp. tritici (Bgt) causes substantial yield losses. Heshangmai (HSM), a wheat landrace originating from Sichuan Province, possesses high levels of resistance to PM. A comprehensive study using a large segregating population of a cross between HSM and Ningmaizi119 (NMZ119) revealed a single recessive allele conferring resistance. The gene, provisionally designated PmHSM, was located on the long arm of chromosome 4A (4AL). Molecular marker analysis, PM response array, and an allelism test indicated that PmHSM is a novel recessive resistance gene that shares an allelic relationship with PmHHXM. Thirteen simple sequence repeat (SSR) markers were developed using the sequence information of the 4AL region in the Chinese spring reference sequence v2.1 (CS RefSeq v2.1). PmHSM was flanked by markers Xmp1567 and Xmp1444 at genetic distances of 0.11 cM and 0.18 cM, respectively, and co-segregated with markers Xmp1439/Xmp1440/Xmp1442.

Study of metabolite differences of flue-cured tobacco from Canada (CT157) and Yunnan (Yunyan 87).

In order to comprehend the dissimilarities in tobacco quality between Canada and Yunnan, a comparison of the aroma components was conducted using GC-MS and HPLC analysis, coupled with orthogonal partial least squares discriminant analysis (OPLS-DA). The study revealed the detection of a total of 81 aroma components and 22 non-volatile components in both varieties of tobacco leaves. Specifically, there were 102 components of Canada tobacco leaves and 103 components of Yunnan tobacco leaves. Subsequently, a screening was performed on these two types of tobacco leaves, identifying 51 differential components, which accounted for approximately 49.5 % of the overall components detected. Among these, Canada tobacco exhibited a higher concentration of 22 components, comprising roughly 36.4 % of the total, which were primarily composed of semi-volatile organic acids and sesquiterpenes. On the other hand, Yunnan tobacco was characterized by a comparatively higher content of 43 components, constituting approximately 63.6 %, including fatty acid esters, phenols, diterpenes, sugars, and amino acids. Comparatively, Canada tobacco demonstrated elevated levels of fatty acids and sesquiterpenes, while the content of fatty acid esters and diterpenes was relatively lower. These distinctions in aroma components potentially contribute to the varied sensory aroma profiles exhibited by the two types of tobacco.

Genetic diversity and population structure of wheat landraces in Southern Winter Wheat Region of China.

BACKGROUND: Wheat landraces are considered a valuable source of genetic diversity for breeding programs. It is useful to evaluate the genetic diversity in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. In addition, constructing a core germplasm set that represents the genetic diversity of the entire variety set is of great significance for the efficient conservation and utilization of wheat landrace germplasms. RESULTS: To understand the genetic diversity in wheat landrace, 2,023 accessions in the Jiangsu Provincial Crop Germplasm Resource Bank were used to explore the molecular diversity and population structure using the Illumina 15 K single nucleotide polymorphism (SNP) chip. These accessions were divided into five subpopulations based on population structure, principal coordinate and kinship analysis. A significant variation was found within and among the subpopulations based on the molecular variance analysis (AMOVA). Subpopulation 3 showed more genetic variability based on the different allelic patterns (Na, Ne and I). The M strategy as implemented in MStratv 4.1 software was used to construct the representative core collection. A core collection with a total of 311 accessions (15.37%) was selected from the entire landrace germplasm based on genotype and 12 different phenotypic traits. Compared to the initial landrace collections, the core collection displayed higher gene diversity (0.31) and polymorphism information content (PIC) (0.25), and represented almost all phenotypic variation. CONCLUSIONS: A core collection comprising 311 accessions containing 100% of the genetic variation in the initial population was developed. This collection provides a germplasm base for effective management, conservation, and utilization of the variation in the original set.

Sulfadiazine detection in aquatic products using upconversion nanosensor based on photo-induced electron transfer with imidazole ligands and copper ions.

Contamination of aquatic products with sulfonamide antibiotics poses a threat to consumer health and can lead to the emergence of drug-resistant bacteria. Common methods to detect such compounds are slow and require expensive instruments. We developed a sensitive sulfadiazine (SDZ) detection method based on the photoinduced electron transfer between UCNPs and Cu2+. The surface-modified upconversion nanoparticles bind to Cu2+ by electrostatic adsorption, causing fluorescence quenching. The quenched fluorescence was subsequently recovered by the addition of imidazole and SDZ to the detection system, which formed a complex with Cu2+. The sensor showed excellent linearity over a wide concentration range (0.05-1000 ng/mL), had a low limit of detection (0.04 ng/mL), was selective, and was not affected by common substances present in aquatic media. This indicates that the sensor has great potential for application in the detection of SDZ residues in aquatic products.

Generalized ratiometric surface-enhanced Raman scattering biosensor for okadaic acid in food based on Au-triggered signal amplification.

BACKGROUND: Reliability and robustness have been recognized as key challenges for Surface-enhanced Raman scattering (SERS) analytical techniques. Quantifying the concentration of an analyte using a single characteristic peak from SERS has been a controversial topic because the Raman signal is susceptible to highly concentrated electromagnetic hotspots, inhomogeneity of SERS substrate, or non-standardization of measurement conditions. Ratiometric SERS strategies have been demonstrated as a promising solution to effectively balance and compensate for signal fluctuations caused by matrix heterogeneity. However, it is not easy to construct ratiometric SERS sensors with monitoring the ratio of two different signal intensities for target analysis. RESULTS: An attempt has been made to develop a novel ratiometric biosensor that can be applied to detect okadaic acid (OA). Aptamer-anchored magnetic particles were first combined with gold-tagged short complementary DNA (Au-cDNA) to create heterogeneous nanostructures. When the target was present, the Au-cDNA was dissociated from nanostructures, and 4-nitrothiophenol (4-NTP) was initiated to reduce to 4-aminothiophenol (4-ATP) in the presence of hydrogen sources. The SERS ratio change of 4-NTP and 4-ATP was finally detected by AuNPs-coated film. OA was successfully quantified, and the detection limit was as low as 2.4524 ng/mL. The constructed biosensor had good stability and reproducibility with a relative standard deviation of less than 4.47%. The proposed method used gold nanoparticles as an intermediate to achieve catalytic signal amplification and subsequently increased the sensitivity of the biosensor. SIGNIFICANCE AND NOVELTY: Catalytic reaction-based ratiometric SERS biosensors combine the multiple advantages of catalytic signal amplification and signal self-calibration and provide new insights into the development of stable, reproducible, and reliable SERS detection techniques. This ratiometric SERS technique offered a universal method that is anticipated to be applicable for the detection of other targets by substituting the aptamer.

Upconversion fluorescence nanosensor based on enzymatic inhibited and copper-triggered o-phenylenediamine oxidation for the detection of dimethoate pesticides.

Pesticide residues in agricultural products pose a significant threat to human health. Herein, a sensitive fluorescence method employing upconversion nanoparticles was developed for detecting organophosphorus pesticides (OPs) based on the principle of enzyme inhibition and copper-triggered o-phenylenediamine (OPD) oxidation. Copper ions (Cu2+) oxidized the colorless OPD to a yellow 2,3-diaminophenazine (oxOPD). The yellow solution oxOPD quenched the fluorescence of upconversion nanoparticles due to the fluorescence resonance energy transfer. The high affinity of Cu2+ for thiocholine reduced the level of oxOPD, resulting in almost no fluorescence quenching. The addition of dimethoate led to the inhibition of acetylcholinesterase activity and thus prevented the formation of thiocholine. Subsequently, Cu2+ oxidized OPD to form oxOPD, which attenuated the fluorescence signal of the system. The detection system has a good linear range of 0.01 ng/mL to 50 ng/mL with a detection limit of 0.008 ng/mL, providing promising applications for rapid detection of dimethoate.

Upconversion Nanoparticles Based Sensing: From Design to Point-of-Care Testing.

Rare earth-doped upconversion nanoparticles (UCNPs) have achieved a wide range of applications in the sensing field due to their unique anti-Stokes luminescence property, minimized background interference, excellent biocompatibility, and stable physicochemical properties. However, UCNPs-based sensing platforms still face several challenges, including inherent limitations from UCNPs such as low quantum yields and narrow absorption cross-sections, as well as constraints related to energy transfer efficiencies in sensing systems. Therefore, the construction of high-performance UCNPs-based sensing platforms is an important cornerstone for conducting relevant research. This work begins by providing a brief overview of the upconversion luminescence mechanism in UCNPs. Subsequently, it offers a comprehensive summary of the sensors' types, design principles, and optimized design strategies for UCNPs sensing platforms. More cost-effective and promising point-of-care testing applications implemented based on UCNPs sensing systems are also summarized. Finally, this work addresses the future challenges and prospects for UCNPs-based sensing platforms.

Enhanced detection of acrylamide using a versatile solid-state upconversion sensor through spectral and visual analysis.

Acrylamide (AM) generally forms in high-temperature processes and has been classified as a potential carcinogen. In this study, we put forward a maneuverable solid-state luminescence sensor using polydimethylsiloxane (PDMS) as the matrix coupled with upconversion nanoparticles as the indicator. The core-shell upconversion nanoparticles emitting cyan light were uniformly encapsulated in PDMS. Then it was further modified with complementary DNA of AM aptamer. The nanocrystalline fluorescein isothiocyanate isomer (FITC), coupled with AM aptamer, was attached to the surface of PDMS. FITC effectively quenched the upconversion luminescence through fluorescence resonance energy transfer (FRET). The introduction of AM resulted in preferentially bound to aptamer caused the separation of the quencher and the donor, and led to luminescence recovery. The developed sensor was applied for both spectral and visual monitoring, demonstrating a detection limit (LOD) of 1.00 nM and 1.07 nM, respectively. Importantly, in the actual foodstuffs detection, there is no obvious difference between the results of this study and the standard method, which indicates the developed method has good accuracy. Therefore, this solid-state sensor has the potential for on-site detection using a smartphone device and an Android application.

Wheat Pm55 alleles exhibit distinct interactions with an inhibitor to cause different powdery mildew resistance.

Powdery mildew poses a significant threat to wheat crops worldwide, emphasizing the need for durable disease control strategies. The wheat-Dasypyrum villosum T5AL·5 V#4 S and T5DL·5 V#4 S translocation lines carrying powdery mildew resistant gene Pm55 shows developmental-stage and tissue-specific resistance, whereas T5DL·5 V#5 S line carrying Pm5V confers resistance at all stages. Here, we clone Pm55 and Pm5V, and reveal that they are allelic and renamed as Pm55a and Pm55b, respectively. The two Pm55 alleles encode coiled-coil, nucleotide-binding site-leucine-rich repeat (CNL) proteins, conferring broad-spectrum resistance to powdery mildew. However, they interact differently with a linked inhibitor gene, SuPm55 to cause different resistance to wheat powdery mildew. Notably, Pm55 and SuPm55 encode unrelated CNL proteins, and the inactivation of SuPm55 significantly reduces plant fitness. Combining SuPm55/Pm55a and Pm55b in wheat does not result in allele suppression or yield penalty. Our results provide not only insights into the suppression of resistance in wheat, but also a strategy for breeding durable resistance.

A dual-mode fluorescence and colorimetric sensing platform for efficient detection of ofloxacin in aquatic products using iron alkoxide nanozyme.

Trace detection of ofloxacin (OFL) with high sensitivity, reliability, and visual clarity is challenging. To address this, a novel dual-modal aptasensor with fluorescence-colorimetric capabilities was designed that exploit the target-induced release of 3,3',5,5'-tetramethylbenzidine (TMB) molecules from aptamer-gated mesoporous silica nanoparticles (MSNs), the oxidase-like activity of iron alkoxide (IA) nanozyme, and the fluorescence attributes of core-shell upconversion nanoparticles. Therefore, the study reports a dual mode detection, with a fluorescence detection range for OFL spanning from 0.1 µg/kg to 1000 µg/kg (and a detection limit of 0.048 µg/kg). Additionally, the colorimetric method offered a linear detection range of 0.3 µg/kg to 1000 µg/kg, with a detection limit of 0.165 µg/kg. The proposed biosensor had been successfully applied to the determination of OFL content in real samples with satisfactory recoveries (78.24-96.14 %).

Estimating BDS-3 Satellite Differential Code Biases with the Single-Frequency Uncombined PPP Model.

Differential Code Bias (DCB) is a crucially systematic error in satellite positioning and ionospheric modeling. This study aims to estimate the BeiDou-3 global navigation satellite system (BDS-3) satellite DCBs by using the single-frequency (SF) uncombined Precise Point Positioning (PPP) model. The experiment utilized BDS-3 B1 observations collected from 25 International GNSS Service (IGS) stations located at various latitudes during March 2023. The results reveal that the accuracy of estimating B1I-B3I DCBs derived from single receiver exhibits latitude dependence. Stations in low-latitude regions show considerable variability in the root mean square (RMS) of absolute offsets for satellite DCBs estimation, covering a wide range of values. In contrast, mid- to high-latitude stations demonstrate a more consistent pattern with relatively stable RMS values. Moreover, it has been observed that the stations situated in the Northern Hemisphere display a higher level of consistency in the RMS values when compared to those in the Southern Hemisphere. When incorporating estimates from all 25 stations, the RMS of the absolute offsets in satellite DCBs estimation consistently remained below 0.8 ns. Notably, after excluding 8 low-latitude stations and utilizing data from the remaining 17 stations, the RMS of absolute offsets in satellite DCBs estimation decreased to below 0.63 ns. These enhancements underscore the importance of incorporating a sufficient number of mid- and high-latitude stations to mitigate the effects of ionospheric variability when utilizing SF observations for satellite DCBs estimation.

Enhanced detection of endocrine disrupting chemicals in on-chip microfluidic biosensors using aptamer-mediated bridging flocculation and upconversion luminescence.

Exposure to endocrine-disrupting chemicals (EDCs) can lead to detrimental impacts on human health, making their detection a critical issue. A novel approach utilizing on-chip microfluidic biosensors was developed for the simultaneous detection of two EDCs, namely, bisphenol A (BPA) and diethylstilbestrol (DES), based on upconversion nanoparticles doped with thulium (Tm) and erbium (Er), respectively. From the perspective of single nanoparticles, the construction of an active core-inert shell structure enhanced the luminescence of nanoparticles by 2.28-fold (Tm) and 1.72-fold (Er). From the perspective of the nanoparticle population, the study exploited an aptamer-mediated bridging flocculation mechanism and effectively enhanced the upconversion luminescence of biosensors by 8.94-fold (Tm) and 7.10-fold (Er). A chip with 138 tangential semicircles or quarter-circles was designed and simulated to facilitate adequate mixing, reaction, magnetic separation, and detection conditions. The on-chip microfluidic biosensor demonstrated exceptional capabilities for the simultaneous detection of BPA and DES with ultrasensitive detection limits of 0.0076 µg L-1, and 0.0131 µg L-1, respectively. The first reported aptamer-mediated upconversion nanoparticle bridging flocculation provided enhanced luminescence and detection sensitivity for biosensors, as well as offering a new perspective to address the instability of nanobiosensors.

Genetic basis of resistance against powdery mildew in the wheat cultivar "Tabasco".

European winter wheat cultivar "Tabasco" was reported to have resistance to powdery mildew disease caused by Blumeria graminis f. sp. tritici (Bgt) in China. In previous studies, Tabasco was reported to have the resistance gene designated as Pm48 on the short arm of chromosome 5D when a mapping population was phenotyped with pathogen isolate Bgt19 collected in China and was genotyped with simple sequence repeat (SSR) markers. In this study, single-nucleotide polymorphism (SNP) chips were used to rapidly determine the resistance gene by mapping a new F2 population that was developed from Tabasco and a susceptible cultivar "Ningmaizi119" and inoculated with pathogen isolate NCF-D-1-1 that was collected in the USA. The segregation of resistance in the population was found to link with Pm2 which was identified in Tabasco. Therefore, it was concluded that the previously reported Pm48 on chromosome arm 5DS in Tabasco should be the Pm2 gene on the same chromosome. The Pm2 was also found in European cultivars "Mattis" and "Claire" but not in any of the accessions from diploid wheat Aegilops tauschii or modern cultivars such as "Gallagher," "Smith's Gold," and "OK Corral" being used in the Great Plains in the USA. A KASP marker was developed to track the resistance allele Pm2 in wheat breeding. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01402-3.

On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods.

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.

Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators.

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

Feasibility of an NIR spectral calibration transfer algorithm based on optimized feature variables to predict tobacco samples in different states.

The prediction accuracy of calibration models for near-infrared (NIR) spectroscopy typically relies on the morphology and homogeneity of the samples. To achieve non-homogeneous tobacco samples for non-destructive and rapid analysis, a method that can predict tobacco filament samples using reliable models based on the corresponding tobacco powder is proposed here. First, as it is necessary to establish a simple and robust calibrated model with excellent performance, based on full-wavelength PLSR (Full-PLSR), the key feature variables were screened by three methods, namely competitive adaptive reweighted sampling (CARS), variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV), and variable combination population analysis-genetic algorithm (VCPA-GA). The partial least squares regression (PLSR) models for predicting the total sugar content in tobacco were established based on three optimal wavelength sets and named CARS-PLSR, VCPA-IRIV-PLSR and VCPA-GA-PLSR, respectively. Subsequently, they were combined with different calibration transfer algorithms, including calibration transfer based on canonical correlation analysis (CTCCA), slope/bias correction (S/B) and non-supervised parameter-free framework for calibration enhancement (NS-PFCE), to evaluate the best prediction model for the tobacco filament samples. Compared with the previous two transfer algorithms, NS-PFCE performed the best under various wavelength conditions. The prediction results indicated that the most successful approach for predicting the tobacco filament samples was achieved by VCPA-IRIV-PLSR when coupled with the NS-PFCE method, which obtained the highest determination coefficient (Rp2 = 0.9340) and the lowest root mean square error of the prediction set (RMSEP = 0.8425). VCPA-IRIV simplifies the calibration model and improves the efficiency of model transfer (31 variables). Furthermore, it pledges the prediction accuracy of the tobacco filament samples when combined with NS-PFCE. In summary, calibration transfer based on optimized feature variables can eliminate prediction errors caused by sample morphological differences and proves to be a more beneficial method for online application in the tobacco industry.

Selective detection of carbendazim using a upconversion fluorescence sensor modified by biomimetic molecularly imprinted polymers.

The persistence of carbendazim residues in the food chain poses a potential risk to human health. Therefore, an eco-friendly selective and sensitive fluorescence nanosensor was established for carbendazim determination based on molecularly imprinted polymer (MIP) modified upconversion nanoparticles (UCNPs). The molecularly imprinted coating with methacrylamide as a functional monomer and carbendazim as a template molecule grafted on the UCNPs (UCNPs@MIP) constituted fluorescent recognition elements. The fluorescence emission of UCNPs@MIP significantly declined in the presence of carbendazim due to electron transfer induced by its selective binding with MIP cavities. The quenched fluorescence of UCNPs@MIP was recovered once the template carbendazim was eluted from the probe system. Under the optimized conditions, the proposed method offers a good linear correlation between 0.01 and 1 µg/mL, with a limit of detection (LOD) of 0.0036 µg/mL for carbendazim residues. The analytical utility and reliability of the developed biomimetic platform were examined in real food samples with good recoveries (88.790%∼102.675%) and relative standard deviation (RSD) values (0.491%∼3.779%). The method was further validated by a standard HPLC method in terms of student's t-test (p > 0.05) with no significant differences between the two methods. Hence, the proposed fluorescence sensor has prospects for rapid determination of carbendazim.

Bipyridine-mediated fluorescence charge transfer process based on copper ion grafted upconversion nanoparticle platform for ciprofloxacin sensing in aquatic products.

In this work, an ultrasensitive ciprofloxacin (CIP) detection strategy has been established based on copper (Cu2+) ions-induced strong charge transfer in poly acrylic acid (PAA) functionalized upconversion nanoparticles (UCNPs)/2,2-bipyridine (bipy) system. The positively charged Cu2+ ions electrostatically adhere to the surface of the PAA-UCNPs and deactivate the fluorescence via a charge transfer process. The bipy in this hybrid system controls the aggregation by chelating in proximity to the Cu2+ center. Due to the strong affinity between pyridone oxygen and carboxy oxygen, CIP coordinates in high stoichiometry with the bipy-Cu complex as compared to the PAA-UCNPs, causing the trapped fluorescence to be released in an amount equivalent to the target concentration. Under the optimum assay conditions, a good calibration plot (0.05-1000 ng/mL) was acquired with a detection limit of 0.13 ng/mL. The satisfactory recoveries (85.93-96.87%) for real prawn and fish samples were further validated by enzyme-linked immunoassays (P > 0.05).

Competitive Ratiometric Aptasensing with Core-Internal Standard-Shell Structure Based on Surface-Enhanced Raman Scattering.

Reproducibility and stability are important indicators for the evaluation of quantitative sensing methods based on surface-enhanced Raman scattering (SERS) technology. Developing a SERS substrate with self-calibration capabilities is vital for effectively quantifying targets. In this work, a competitive ratiometric SERS aptasensor was developed. 4-Aminothiophenol as an internal standard (IS) was embedded in the substrate followed by gradually loading with the aptamer and methylene blue functionalizing of the complementary sequences of the aptamer (MB-cDNA). Recognition and binding of the target to the aptamer resulted in the shedding of MB-cDNA after magnetic separation reducing the SERS signal of MB, allowing for the ratiometric determination of the target based on the constant intensity from the IS. For the selective detection of okadaic acid (OA), a good negative correlation was achieved between the SERS ratiometric intensity and OA concentration in the range of 0.5-100 ng/mL. The magnetic separation strategy effectively simplifies the production steps of the aptasensor, and the ratiometric strategy effectively improved the reproducibility and stability of the OA sensing. This ratiometric aptasensor has been successfully employed to detect OA in food and environmental samples and is expected to be extended to detect other targets.

Across different instruments about tobacco quantitative analysis model of NIR spectroscopy based on transfer learning.

With the development of near-infrared (NIR) spectroscopy, various calibration transfer algorithms have been proposed, but such algorithms are often based on the same distribution of samples. In machine learning, calibration transfer between types of samples can be achieved using transfer learning and does not need many samples. This paper proposed an instance transfer learning algorithm based on boosted weighted extreme learning machine (weighted ELM) to construct NIR quantitative analysis models based on different instruments for tobacco in practical production. The support vector machine (SVM), weighted ELM, and weighted ELM-AdaBoost models were compared after the spectral data were preprocessed by standard normal variate (SNV) and principal component analysis (PCA), and then the weighted ELM-TrAdaBoost model was built using data from the other domain to realize the transfer from different source domains to the target domain. The coefficient of determination of prediction (R 2) of the weighted ELM-TrAdaBoost model of four target components (nicotine, Cl, K, and total nitrogen) reached 0.9426, 0.8147, 0.7548, and 0.6980. The results demonstrated the superiority of ensemble learning and the source domain samples for model construction, improving the models' generalization ability and prediction performance. This is not a bad approach when modeling with small sample sizes and has the advantage of fast learning.