Automating insect monitoring using unsupervised near-infrared sensors.

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.

The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology.

In recent years, numerous applications for artificial intelligence (AI) in cardiology have been found, due in part to large digitized data sets and the evolution of high-performance computing. In the discipline of cardiac electrophysiology (EP), a number of clinical, imaging, and electrical waveform data are considered in the diagnosis, prognostication, and management of arrhythmias, which lend themselves well to automation through AI. But equally relevant, AI offers a unique opportunity to discover novel EP concepts and improve clinical care through its inherent, hierarchical tenets of self-learning. In this review we focus on the application of AI in clinical EP and summarize state-of-the art, large, clinical studies in the following key domains: (1) electrocardiogram-based arrhythmia and disease classification; (2) atrial fibrillation source detection; (3) substrate and risk assessment for atrial fibrillation and ventricular tachyarrhythmias; and (4) predicting outcomes after cardiac resynchronization therapy. Many are small, single-centre, proof-of-concept investigations, but they still show ground-breaking performance of deep learning, a subdomain of AI, which surpasses traditional statistical analysis. Larger studies, for instance classifying arrhythmias from electrocardiogram recordings, have further provided external validation of their high accuracy. Ultimately, the performance of AI is dependent on the quality of the input data and the rigour of algorithm development. The field is still nascent and several barriers will need to be overcome, including prospective validation in large, well labelled data sets and more seamless information technology-based data collection/integration, before AI can be adopted into broader clinical EP practice. This review concludes with a discussion of these challenges and future work.

Live-imaging rate-of-kill compound profiling for Chagas disease drug discovery with a new automated high-content assay.

Chagas disease, caused by the protozoan intracellular parasite Trypanosoma cruzi, is a highly neglected tropical disease, causing significant morbidity and mortality in central and south America. Current treatments are inadequate, and recent clinical trials of drugs inhibiting CYP51 have failed, exposing a lack of understanding of how to translate laboratory findings to the clinic. Following these failures many new model systems have been developed, both in vitro and in vivo, that provide improved understanding of the causes for clinical trial failures. Amongst these are in vitro rate-of-kill (RoK) assays that reveal how fast compounds kill intracellular parasites. Such assays have shown clear distinctions between the compounds that failed in clinical trials and the standard of care. However, the published RoK assays have some key drawbacks, including low time-resolution and inability to track the same cell population over time. Here, we present a new, live-imaging RoK assay for intracellular T. cruzi that overcomes these issues. We show that the assay is highly reproducible and report high time-resolution RoK data for key clinical compounds as well as new chemical entities. The data generated by this assay allow fast acting compounds to be prioritised for progression, the fate of individual parasites to be tracked, shifts of mode-of-action within series to be monitored, better PKPD modelling and selection of suitable partners for combination therapy.

Development and validation of a high-content screening assay for inhibitors of enteropathogenic E. coli adhesion.

Enteropathogenic E. coli (EPEC) causes intestinal infections leading to severe diarrhea. EPEC attaches to the host cell causing lesions to the intestinal epithelium coupled with the effacement of microvilli. In the process, actin accumulates into a pedestal-like structure under bacterial microcolonies. We designed an automated fluorescence microscopy-based screening method for discovering compounds capable of inhibiting EPEC adhesion and virulence using aurodox, a type three secretion system (T3SS) inhibitor, as a positive control. The screening assay employs an EPEC strain (2348/69) expressing a fluorescent protein and actin staining for monitoring the bacteria and their pedestals respectively, analyzing these with a custom image analysis pipeline. The assay allows for the discovery of compounds capable of preventing the formation of pathogenic actin rearrangements. These compounds may be interfering with virulence-related molecular pathways relevant for developing antivirulence leads.

An open-source drug discovery platform enables ultra-large virtual screens.

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

Online coupling of the Ussing chamber, solid-phase extraction and high-performance liquid chromatography for screening and analysis of active constituents of traditional Chinese medicines.

A semi-automated online platform was established successfully for preliminary screening of potential active flavonoids of traditional Chinese medicines (TCMs) in multicomponent system. Online coupling of the in vitro intestinal absorption model, solid phase extraction (SPE) and high-performance liquid chromatography (HPLC) was actualized at the first time. The Ussing chamber model was selected to absorb the constituents of TCMs. A mini chromatographic column filled with C18 was used as a SPE column for online enrichment of flavonoids. HPLC was applied to analyze the constituents screened by platform. With the use of rutin as a model flavonoid, the specifications of SPE column, eluting solvent, elution time and flow rate of eluent were systematically investigated to optimize online system. Under the optimal conditions, the linear range of rutin was 0.125-368 µg/mL with the correlation coefficient (R2) greater than 0.9947. The limit of detection (LOD) was as low as 0.0500 µg/mL and the limit of quantification (LOQ) was 0.125 µg/mL. The intra-day relative standard deviation (RSD) and inter-day RSD was 2.5% and 3.8%, respectively. The recoveries of rutin in the intestinal absorption samples ranged from 93.2% to 94.0%. Finally, the online system was applied to screen the potential active flavonoids of Scutellaria baicalensis Georgi (Huangqin, HQ) and Polygoni Cuspidati Rhizoma et Radix (Huzhang, HZ). A total of 14 flavonoids of these two TCMs were identified by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and 12 flavonoids of them were screened as the potential active components by online Ussing chamber-SPE-HPLC. In comparison with offline method and gavage in rats, the online system can screen the active constituents from TCMs more accurately and completely. The results demonstrated that the online system was reliable and sufficiently accurate for screening and determination of the potential active flavonoids of TCMs in multicomponent system.

User-centered practicability analysis of two identification strategies in electrode arrays for FES induced hand motion in early stroke rehabilitation.

BACKGROUND: Surface electrode arrays have become popular in the application of functional electrical stimulation (FES) on the forearm. Arrays consist of multiple, small elements, which can be activated separately or in groups, forming virtual electrodes (VEs). As technology progress yields rising numbers of possible elements, an effective search strategy for suitable VEs in electrode arrays is of increasing importance. Current methods can be time-consuming, lack user integration, and miss an evaluation regarding clinical acceptance and practicability. METHODS: Two array identification procedures with different levels of user integration-a semi-automatic and a fully automatic approach-are evaluated. The semi-automatic method allows health professionals to continuously modify VEs via a touchscreen while the stimulation intensities are automatically controlled to maintain sufficient wrist extension. The automatic approach evaluates stimulation responses of various VEs for different intensities using a cost function and joint-angles recordings. Both procedures are compared in a clinical setup with five sub-acute stroke patients with moderate hand disabilities. The task was to find suitable VEs in two arrays with 59 elements in total to generate hand opening and closing for a grasp-and-release task. Practicability and acceptance by patients and health professionals were investigated using questionnaires and interviews. RESULTS: Both identification methods yield suitable VEs for hand opening and closing in patients who could tolerate the stimulation. However, the resulting VEs differed for both approaches. The average time for a complete search was 25% faster for the semi-automatic approach (semi-automatic: 7.3min, automatic: 10.5min). User acceptance was high for both methods, while no clear preference could be identified. CONCLUSIONS: The semi-automatic approach should be preferred as the search strategy in arrays on the forearm. The observed faster search duration will further reduce when applying the system repeatedly on a patient as only small position adjustments for VEs are required. However, the setup time will significantly increase for generation of various grasp types and adaptation to different arm postures. We recommend different levels of user integration in FES systems such that the search strategy can be chosen based on the users' preferences and application scenario.

Quantitative on-line analysis of sulfur compounds in complex hydrocarbon matrices.

An improved method for on-line measurement of sulfur containing compounds in complex matrices is presented. The on-line system consists of a specifically designed sampling system connected to a comprehensive two-dimensional gas chromatograph (GC×GC) equipped with two capillary columns (Rtx®-1 PONA×SGE BPX50), a flame ionization detector (FID) and a sulfur chemiluminescence detector (SCD). The result is an unprecedented sensitivity down to ppm level (1 ppm-w) for various sulfur containing compounds in very complex hydrocarbon matrices. In addition to the GC×GC-SCD, the low molecular weight sulfur containing compounds such as hydrogen sulfide (H2S) and carbonyl sulfide (COS) can be analyzed using a thermal conductivity detector of a so-called refinery gas analyzer (RGA). The methodology was extensively tested on a continuous flow pilot plant for steam cracking, in which quantification of sulfur containing compounds in the reactor effluent was carried out using 3-chlorothiophene as internal standard. The GC×GC-FID/-SCD settings were optimized for ppm analysis of sulfur compounds in olefin-rich (ethylene- and propylene-rich) hydrocarbon matrices produced by steam cracking of petroleum feedstocks. Besides that is primarily used for analysis of the hydrocarbon matrix, FID of the GC×GC-FID/-SCD set-up serves to double check the amount of added sulfur internal standard which is crucial for a proper quantification of sulfur compounds. When vacuum gas oil containing 780 ppm-w of elemental sulfur in the form of benzothiophenes and dibenzothiophenes is subjected to steam cracking, the sulfur balance was closed, with 75% of the sulfur contained in the feed is converted to hydrogen sulfide, 13% to alkyl homologues of thiophene while the remaining 12% is present in the form of alkyl homologues of benzothiophenes. The methodology can be applied for many other conversion processes which use sulfur containing feeds such as hydrocracking, catalytic cracking, kerogen evolution, bio-waste pyrolysis, supercritical water treatment, etc.

Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors.

Organ-on-a-chip systems are miniaturized microfluidic 3D human tissue and organ models designed to recapitulate the important biological and physiological parameters of their in vivo counterparts. They have recently emerged as a viable platform for personalized medicine and drug screening. These in vitro models, featuring biomimetic compositions, architectures, and functions, are expected to replace the conventional planar, static cell cultures and bridge the gap between the currently used preclinical animal models and the human body. Multiple organoid models may be further connected together through the microfluidics in a similar manner in which they are arranged in vivo, providing the capability to analyze multiorgan interactions. Although a wide variety of human organ-on-a-chip models have been created, there are limited efforts on the integration of multisensor systems. However, in situ continual measuring is critical in precise assessment of the microenvironment parameters and the dynamic responses of the organs to pharmaceutical compounds over extended periods of time. In addition, automated and noninvasive capability is strongly desired for long-term monitoring. Here, we report a fully integrated modular physical, biochemical, and optical sensing platform through a fluidics-routing breadboard, which operates organ-on-a-chip units in a continual, dynamic, and automated manner. We believe that this platform technology has paved a potential avenue to promote the performance of current organ-on-a-chip models in drug screening by integrating a multitude of real-time sensors to achieve automated in situ monitoring of biophysical and biochemical parameters.

Automated Control of the Organic and Inorganic Composition of Aloe vera Extracts Using (1)H NMR Spectroscopy.

Recent classification of Aloe vera whole-leaf extract by the International Agency for Research and Cancer as a possible carcinogen to humans as well as the continuous adulteration of A. vera's authentic material have generated renewed interest in controlling A. vera. The existing NMR spectroscopic method for the analysis of A. vera, which is based on a routine developed at Spectral Service, was extended. Apart from aloverose, glucose, malic acid, lactic acid, citric acid, whole-leaf material (WLM), acetic acid, fumaric acid, sodium benzoate, and potassium sorbate, the quantification of Mg(2+), Ca(2+), and fructose is possible with the addition of a Cs-EDTA solution to sample. The proposed methodology was automated, which includes phasing, baseline-correction, deconvolution (based on the Lorentzian function), integration, quantification, and reporting. The NMR method was applied to 41 A. vera preparations in the form of liquid A. vera juice and solid A. vera powder. The advantages of the new NMR methodology over the previous method were discussed. Correlation between the new and standard NMR methodologies was significant for aloverose, glucose, malic acid, lactic acid, citric acid, and WLM (P < 0.0001, R(2) = 0.99). NMR was found to be suitable for the automated simultaneous quantitative determination of 13 parameters in A. vera.

Online solid-phase extraction with high-performance liquid chromatography and mass spectrometry for the determination of five tannins in traditional Chinese medicine injections.

A rapid analytical method based on online solid-phase extraction with high-performance liquid chromatography and mass spectrometry has been established and applied to the determination of tannin compounds that may cause adverse effects in traditional Chinese medicine injections. Different solid-phase extraction sorbents have been compared and the elution buffer was optimized. The performance of the method was verified by evaluation of recovery (≥40%), repeatability (RSD ≤ 6%), linearity (r(2) ≥ 0.993), and limit of quantification (≤0.35 µg/mL). Five tannin compounds, gallic acid, cianidanol, gallocatechin gallate, ellagic acid, and penta-O-galloylglucose, were identified with concentrations ranging from 3.1-37.4 µg/mL in the analyzed traditional Chinese medicine injections.

Serum calcitriol concentrations measured with a new direct automated assay in a large population of adult healthy subjects and in various clinical situations.

The measurement of calcitriol [1,25(OH2)D], is important for the differential diagnosis of several disorders of calcium/phosphorus metabolism but is time-consuming and tricky. We measured serum calcitriol with a new automated direct assay on the Liaison XL platform in 888 healthy French Caucasian subjects aged 18-89 years, 32 patients with a surgically-proven PHPT, 32 pregnant women at the end of the first and at the end of the third trimester, and 24 dialysis patients before and after one year of supplementation with vitamin D3 or placebo. The mean calcitriol concentration (±SD) in the healthy population was 52.9±14.5 ng/L with a 95% CI interval of 29-83.6 ng/L. In PHPT patients, calcitriol concentration was 81.6±29.0 ng/L, 15 of them (46.9%) having a concentration >83.6 ng/L. In pregnant women, calcitriol was 80.4±26.4 ng/L at the end of the first trimester, and 113.1±33.0 ng/L at the end of the third trimester, 12 (37.5%) and 26 (81.3%) of them having a calcitriol concentration >83.6 ng/L at the first and third trimesters respectively. In 14 dialysis patients, calcitriol was 9.5±7.7 ng/L and rose to 19.3 ng/L after one year of supplementation with 50,000 IU vitamin D3/month. In 10 other dialysis patients, calcitriol was 9.9±2.9 ng/L and remained stable (12.4±3.7 ng/L) after one year of placebo. In conclusion, this new automated calcitriol assay, in addition to presenting excellent analytical performances, gives the expected variations in patients compared to "normal" values obtained in an extensive reference population.

[Construction of automatic elucidation platform for mechanism of traditional Chinese medicine].

Aim at the two problems in the field of traditional Chinese medicine (TCM) mechanism elucidation, one is the lack of detailed biological processes information, next is the low efficient in constructing network models, we constructed an auxiliary elucidation system for the TCM mechanism and realize the automatic establishment of biological network model. This study used the Entity Grammar Systems (EGS) as the theoretical framework, integrated the data of formulae, herbs, chemical components, targets of component, biological reactions, signaling pathways and disease related proteins, established the formal models, wrote the reasoning engine, constructed the auxiliary elucidation system for the TCM mechanism elucidation. The platform provides an automatic modeling method for biological network model of TCM mechanism. It would be benefit to perform the in-depth research on TCM theory of natures and combination and provides the scientific references for R&D of TCM.

Principles and methods for automated palynology.

Pollen grains are microscopic so their identification and quantification has, for decades, depended upon human observers using light microscopes: a labour-intensive approach. Modern improvements in computing and imaging hardware and software now bring automation of pollen analyses within reach. In this paper, we provide the first review in over 15 yr of progress towards automation of the part of palynology concerned with counting and classifying pollen, bringing together literature published from a wide spectrum of sources. We consider which attempts offer the most potential for an automated palynology system for universal application across all fields of research concerned with pollen classification and counting. We discuss what is required to make the datasets of these automated systems as acceptable as those produced by human palynologists, and present suggestions for how automation will generate novel approaches to counting and classifying pollen that have hitherto been unthinkable.

Graphene/polydopamine-modified polytetrafluoroethylene microtube for the sensitive determination of three active components in Fructus Psoraleae by online solid-phase microextraction with high-performance liquid chromatography.

Determination of bioactive compounds in traditional Chinese medicines and biological samples is usually interfered with by coexisting components in matrices. In this work, we prepared novel multilayer functional graphene/polydopamine-modified polytetrafluoroethylene microtube for selective solid-phase microextraction of three bioactive compounds in Fructus Psoraleae. Functional graphene/polydopamine-modified polytetrafluoroethylene microtube showed good extraction efficiency toward bavachin, isobavachalcone, and bavachinin; enrichment from 357- to 737-fold was obtained for these compounds. For qualitative analysis, an online solid-phase microextraction with high-performance liquid chromatography method was developed, which showed low limits of detection of 0.02 ng/mL by using UV detection, which is significantly more sensitive than previously reported methods. The proposed method has been used to determine bavachin, isobavachalcone, and bavachinin in Fructus Psoraleae, the contents of three compounds were quantified to be 64.0, 324.0, and 384.5 µg/g; recoveries were 93.4-101.1%. The proposed method has also been applied to determine bavachin, isobavachalcone, and bavachinin in rat plasma samples after oral administration of Fructus Psoraleae.

Quantification of soy isoflavones and their conjugative metabolites in plasma and urine: an automated and validated UHPLC-MS/MS method for use in large-scale studies.

The biotransformation of isoflavones by gut microbiota and by drug metabolizing enzymes plays a crucial role in the understanding of their potential health-promoting effects. The purpose of our work was to develop a simultaneous, sensitive, and robust automated ultra high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method to quantify the soy isoflavones daidzein and genistein, their conjugative metabolites, as well as their major microbial degradation products in order to provide a method for use in large clinical trials or animal studies. An automated, 96-well solid-phase extraction method was used to extract the isoflavone analytes from plasma and urine. Separation of genistein, daidzein, and 19 of its metabolites, including five glucuronides, seven sulfates, and two sulfoglucuronides, as well as five microbial metabolites, was achieved in less than 25 min using a sub-2 µm particle column and a gradient elution with acetonitrile/methanol/water as mobile phases. Analysis was performed under negative ionization electrospray MS via the multiple reaction monitoring (MRM). Validation was performed according to the analytical method validation guidelines of Food and Drug Administration (FDA) and International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) consisting of selectivity, accuracy, precision, linearity, limit of detection, recovery, matrix effect, and robustness. All validated parameters essentially matched the FDA and ICH requirements. The application of this method to a pharmacokinetic study in postmenopausal women showed that isoflavones are extensively metabolized in vivo. A robust automated analytical approach was developed, which allows the handling of large sample sizes but nevertheless provides detailed information on the isoflavone metabolite profile leading to a better understanding and interpretation of clinical and animal studies.

The integrated quality assessment of Chinese commercial dry red wine based on a method of online HPLC-DAD-CL combined with HPLC-ESI-MS.

AIM: To apply an integrated quality assessment strategy to investigate the quality of multiple Chinese commercial dry red wine samples. METHOD: A comprehensive method was developed by combining a high performance liquid chromatography-diode array detector-chemiluminescence (HPLC-DAD-CL) online hyphenated system with an HPLC-ESI-MS technique. RESULTS: Chromatographic and H2O2-scavenging active fingerprints of thirteen batches of different, commercially available Chinese dry red wine samples were obtained and analyzed. Twenty-five compounds, including eighteen antioxidants were identified and evaluated. The dominant and characteristic antioxidants in the samples were identified. The relationships between antioxidant potency and the cultivated variety of grape, producing area, cellaring period, and trade mark are also discussed. CONCLUSION: The results provide the feasibility for an integrated quality assessment strategy to be efficiently and objectively used in quality (especially antioxidant activity) assessment and identification of dry red wine.

Automated DNA extraction from pollen in honey.

In recent years, honey has become subject of DNA analysis due to potential risks evoked by microorganisms, allergens or genetically modified organisms. However, so far, only a few DNA extraction procedures are available, mostly time-consuming and laborious. Therefore, we developed an automated DNA extraction method from pollen in honey based on a CTAB buffer-based DNA extraction using the Maxwell 16 instrument and the Maxwell 16 FFS Nucleic Acid Extraction System, Custom-Kit. We altered several components and extraction parameters and compared the optimised method with a manual CTAB buffer-based DNA isolation method. The automated DNA extraction was faster and resulted in higher DNA yield and sufficient DNA purity. Real-time PCR results obtained after automated DNA extraction are comparable to results after manual DNA extraction. No PCR inhibition was observed. The applicability of this method was further successfully confirmed by analysis of different routine honey samples.

Automatic spot preparation and image processing of paper microzone-based assays for analysis of bioactive compounds in plant extracts.

The colorimetric determination of the concentration of phytochemicals in plant extract samples using a spotting automatic system, mobile phone camera and a computer with developed software for quantification is described. Method automation was achieved by using a robotic system for spotting. The instrument was set to disperse the appropriate aliquots of the reagents and sample on a Whatman paper sheet. Spots were photographed and analysed by ImageJ software or by applying the developed MatLab based algorithm. The developed assay was found to be effective, with a linear response at the concentration range of 0.03-0.25g/L for polyphenols. The detection limit of the proposed method is sub 0.03g/L. The paper microzone-based assays for flavonoids and amino acids/peptides were also developed and evaluated as applicable. Comparing the results with conventional PµZP methods demonstrates that both methods yield similar results. At the same time, the proposed method has an attractive advantage in analysis time and repeatability/reproducibility.

[Research on quality parameters of scutellariae radix (formula particles) using on-line NIR in pilot with different extraction process].

The on-line monitor for the changes in the content of baicalin in Scutellariae Radix formula particles during the extraction process was conducted by using near infrared spectroscopy (NIR). High performance liquid chromatography (HPLC) was used as a reference method. Kennard-Stone (KS) was used to divide sample sets, so as to compare different pretreatment methods. The synergy interval partial least squares (SiPLS) was used to screen out modeling wave band to establish partial least-squares models. The relative error method was applied to predict forecast set samples of Scutellariae Radix in three extraction phases. The results showed that the model established by Savitzky-Golay smoothing with 11 points (SG11 points) was the best, with the root mean square with cross validation (RMSECV), root mean square error of correction (RMSEC) and root mean square error of prediction (RMSEP) of baicalin of 0.092 7, 0.134 4 and 0.114 8, respectively, the determination coefficient R2 of greater than 0.99, and the relative error of baicalin content of less than 5%. This indicates that the on-line near infrared reflectance spectroscopy could be applied in on-line monitor and quality control of the extraction process of Scutellariae Radix formula particles.