HORIBA, Ltd. (6856) Earnings Call Transcript & Summary

Hello, everybody. Hello, Alessia. So welcome to this webinar about A-TEEM. You will know everything about A-TEEM, which is a technique, a prisms technique. And what is very interesting in this presentation is all the examples Alessia will go through. So during this presentation, you will be able to ask all your questions. So type your question, and Alessia will answer them. So don't hesitate to answer your question -- to ask your question. And at the end of the presentation, Alessia will come back and answer them. After the presentation, so you will be able to answer some question yourself about your knowledge about A-TEEM and your type of application. So I think it's time to start. So see you just after the presentation, which is half an hour long. So let's start now.

Good morning, and welcome to this webinar focused on the application on the simultaneous absorbance, transmission and fluorescence, excitation-emission matrix method. A-TEEM method can be used for absorbance measurements or fluorescence EEM for multi-component analysis, but its real power is derived from the fact that the collected EEM are collected for the inner filter effect. To access the true power of fluorescence A-TEEM spectroscopy, one needs to employ multi variates software method, such as principal component analysis, classical lease square method and Parallel Factor Analysis to monitor component concentration and reveal unexpected compounds in mixture. This novel method is applied in different application domains, spanning from chemistry to water analysis to the pharma domain. This patented method implemented in the [indiscernible] or in the new system data is able to rapidly adapt to a wide concentration ranges and complex mixtures. It deals with a 5 dimension dataset, 3 for the fluorescence EEM, 2 for the absorbance. So it needs a high molecular specificity. Furthermore, the CCD-based detection is particularly suitable for real-time analysis. And thanks to the low stray light optics, we can reach a high chemical sensitivity. So to summarize, EEM measure all the fluorescence pieces. Absorbance provides information on the components that absorb light, but did not fluoresce as well as information needed to correct the fluorescence spectra for the sample concentration-dependent inner filter effect. These measurements and corrections are performed on a second-time scale. Fluorescence is a highly sensitive technique, but it's also sensitive to sample concentration. The inner filter effect is a well-known physical phenomenon where the detect of fluorescent [ signal ] loses linearity at increased concentration on sample due to absorption and reabsorption of photos in the sample observation area. Consequently, it's well-known that you must always dilute your samples to ensure proper fluorescence [ signals ]. In general, it's recognized that when the optical density of the solution in the fluorescence excitation and emission region is below 0.05, the inner filter effects are superfluous. Note, however, that these effects are always present at any [ OD ] value in the overlapping absorbance and fluorescence emission regions. A clear example. In this picture, we have 2 [indiscernible], each filled with slightly different concentrations of gallic acid. The 2 samples appear to be equally dilute, but have different transmission in the UV. Here, we clearly see a difference in the EEM of gallic acid at this 2 different concentration. Under these conditions, an EEM cannot successfully identify these 2 different EEM fingerprints to be anything other than 2 distinctly different compounds. This is why a traditional EEM from a scanning [ thermometer ] cannot provide good fingerprint results for the chemometrics. The difference in the standard EEM can easily lead to a wrong identification of compounds, simultaneous measurement of absorbance and real-time correction for the inner filter effect is the true molecular fingerprint. Another example on the material signs are the quantum dots. Quantum dots absorb over a broad range and that photoluminescence emission over a narrow range, which can be tuned depending of the material from which the quantum dot is made and the size of the quantum dot. The applications of quantum dot continue to expand, for example, in vivo imaging, light-emitting devices, photo detection or solar energy conversion. We show here a new generation of quantum dots. By avoiding the use of the toxic heavy metal, lead and cadmium, indium phosphide based at quantum dots are a promising alternative that retains the spectral range of size-tunable emission while expanding the scope of applicability of soluble semiconductor emitters. They showed great potential to replace the widely applied cadmium containing quantum dots in the next-generation commercial displays and lighting applications. Here, the fluorescence EEM was first collected on the undiluted sample to find the optimal expectation and emission spectral ranges. In this picture, the emission spectra with and without inner filter effect correction are shown. All spectra are corrected for the [ deducted ] noise, spectral correction and lamp intensity. A and B fluorescence spectra are affected by significant and predictable distortions caused by concentration dependent in a filter effect. In this case, some of the excitation light does not properly reach the center of a cuvette due to the strong light absorbance from the highly concentrated molecules, making the fluorescence intensity at a detector lower than expected. Inner filter effect can significantly affect the fluorescence emission spectral profiles, distorting the general shape, shifting the spectral position of the peak maximum and decrease in the emission intensities. Thus, when recorded with a standard spectra for meter, the detective fluorescence does not coincide with the true fluorescence emission of the sample, even for remote fluorophore concentrations. This fluorescence spectra distortions can be critical in display, lighting and bioimaging application where precise knowledge of the quantum dots emission properties are required. Water quality control. Drinking water treatment plants that primarily use surface water sources are regulated because certain water components are precursor to toxic disinfection by products that may react over time in the distribution system with halogenated disinfectants. These components, namely total dissolved organic carbon, are commonly subject to significant variation in often unpredictable pattern associated with rainfall, snowmelt and other events. The salted organic matter is generally originated from 3 major sources: allochthonous or terrestrial material from soils; [ althacthonous ], or surface water derived of algal or phytoplankton origin; and synthetic organic substances of manmade or industrial origin. GUM in natural waters is composed of a terrigenous mixture of organic compounds. Here, we show an example of how the 18 methods can be used to monitor concentration and composition of this organic matter in a typical surface water source. This picture compared typical EEM and absorbance profiles for raw and finished water samples measured in the same daily sample set. As you can see, the raw water exhibits stronger fluorescence and absorbance at all the wavelengths. In particular, the main fluorescence emission band of the raw water is significantly broader and red shifted compared to the finished water to indicate a significant change in the composition of humic-fulvic compounds with larger molecular weight and aromaticity. EEM provide a wealth of information about this of organic matter, which, in itself, can be very difficult to interpret. To evaluate the quantitative changes in the EEM associated with the treatment, PARAFAC Analysis is applied to all sample to compose the excitation spectra, emissions spectra and concentration loadings for the main fluorescence component. The [indiscernible] data stream platform performs all the necessary spectral correction, quickly assemble EEM in convenient dataset objects to easily manage labels, accessed case and classes and include or exclude data from the analysis with a simple click. Solo from [indiscernible] vector provides the graphical interface to quickly manage and analyze EEM data, create and apply models and interpret results. The dashboard provides the latest readings, time series and tables or trend analysis, removals percentage, threshold for all of these parameters and report on fit statistics and residual evaluation for system performance monitoring contamination detection and early warning alerts. This picture illustrated 3 components resolved in the model each floated as a control plot, representing the cross-product of the excitation and emission spectral loadings. Component 1 was identified as a humic-fulvic component with relatively lower molecular weight and aromaticity compared to the Component 2, which was also identified as a humic-fulvic component. In particular, Component 2 exhibited a significantly broader and red shift excitation emission control than Component 1. Component 3 was identified as a protein-like component and exhibited the deepest UV excitation-emission control. Last picture compares the normalized part of our concentration loadings for the 3 components for the daily raw and finished water samples. In the raw water, the main component was consistently Component 2, whereas in the finished water, Component 1 dominated consistently. The relative concentration of component 3 remained largely unchanged between the treatments. The onset removal of Component 2 is consistent with the expected effects of coagulation to remove iron molecular weight organics more effectively than lower molecular weight species. Most surface water treatment plants remain prone to contaminations by petroleum-derived carcinogens because they lack detector upstream or even [indiscernible]. Early [indiscernible] warnings required that water soluble fluorescence or chromophoric components including benzene, toluene, ethylbenzene and xylene, the so-called BTEX, be discriminated from the eye fluorescein big rounds of the natural discovered organic matter components. Benzene is believed to be the most toxic BTEX component and has a low fluorescent quantum yield in water. In contrast, typical surface water discovered organic matter components at higher fluorescence quantum mill than BTEX compounds. Here, we show a sensitive method for rapid reagent and extraction free detection of all BTEX compounds in typical rows of phased water with respective limits of detection and quantification of 1 and 3 milligram per liter. This A-TEEM and partially square analysis approach serves as an effective screening tool for oil contamination in surface water because of rapid data collection time, high sensitivity impacted billion for oil and PH compounds, minimal sample preparation compared to conventional solid phase extraction and liquid or gas chromatographic analytical methods. Take a typical chardonnay. It's reach, full bodied and buttery. Those qualities don't happen by accident. It's the combination of a variety of ingredients and controlling those ingredients could be the difference between a celebratory toast and a glass of vinegar. The color and finale composition of grape juice and wine are key quality characteristics associated with visual perception, taste and mouth feel. Chromatographic analysis are costly, slow and labor-intensive, and reagent basic colorimetric assays are also slow and don't provide compound discrimination. This study demonstrate the unique capacity of the multidimensional A-TEEM. Dataset results are presenting using both unsupervised multivariate component analysis and calibrated lease squares of regression methods for precise sample classification and phenolic quantification. The new application examples are relevant to characterizing and classifying juice and wine sample as a function of ripening and process-related parameters, including adulteration and storage issues such as oxidation and microbial spoilage. Measurement conditions in the least traceable spectral EEM normalized to water [ rums ] kettering unit, corrected for the influence of inner filter effect, blank substraction and [indiscernible] masking before PARAFAC, CLS and PLS regression analysis using the [indiscernible] vector solo package. Red wines contain several colored and fluorescence component, mostly polyphenolic in nature, which determine various quality parameters, including color and flavor. For this experiment, the samples were measured both when the bottle was freshly opened and after 1 week of exposure to air. All EEMs were normalized based on water [ rums ] kettering units for the defined emission conditions. The complex EEMs clearly exhibit the major controls in the UV excitation-emission range with a major excitation-emission peak around 275 and 309 nanometer. For both the before and after samples, the absorbance spectra exhibited a major extension co-peak around 275 nanometer, a smaller shoulder peak around 320 nanometer and a second minor peak around 520 nanometer. The 275-nanometer peak region is commonly associated at least in part with phenolic compounds, and the 520-nanometer peak region is generally associated with the anthocyanin compounds. Compared to the before sample C, the after sample D exhibited increased extension across the entire absorbance spectrum associated with the oxidation phenomenon. The after EEM B showed stronger emission intensity than the before EEM A for all spectra control features. The complexity of the EEM spectral controls, which comprise multiple overlapping excitation and emission components limits qualitative and quantity visual interpretation to major control elements. Its multivariate analysis is generally applied to the composed EEM components in the terms of quality and quantity. Based on the resolved excitation-emission coordinates, the components are compared to literature values for the tentative identification as shown on this table, and effects of oxidation on niche of the PARAFAC components for the wine sample can be then plotted. Clearly, all the analysis are potential value to industrial wine evaluation and can be extended to a wide variety of application beyond those described in this study. Grapes absorb many chemical compounds more through the skins. The level of the contamination depends on venue proximity to the actual [indiscernible], the duration of the smoke in venue, the ripening stage of the actual grapes and grape vine variety, predicting the potential effect that fermentation, storage and consumption will have a smoked, tainted wine, typically involves quantification of key macro compounds like [ fennel ], OM and [indiscernible]. Many smoke-taint compounds exhibit high fluorescence quantum meals and distinct spectral features, as you can observe in this EEM fingerprints. The 18 method coupled to PARAFAC and PLS is well fitted to identify and quantify low concentration of specific analyzed, among other compounds at much higher concentrations. Reported sensory threshold of many of the smoke-taint volatiles are generally 30-milligram per liter or less and require highly sensitive chromatography. Bottled wines from smoked, expensive grapes can exhibit distinct absorbance and fluorescence spectral differences in the UV region. In general, analysis involve laborious, time-consuming sample preparation to separate or liberate free volatiles from the bound before analysis. Here, by using the 18 method, each wine sample was just diluted in 50% of ethanol adjusted to PH 2. In this course, for the first time, EEM fluorescence spectroscopy was combined with the PARAFAC Analysis and they're quite -- was set of 320 dry wet wines of the chardonnay grape variety. So for the inside is the most common chemical compound used in the 1 method. So for [indiscernible], it presents the wine from reacting with oxidant, which can cause browning and oxidation. And it inhibit the growth of bacteria [indiscernible] in the grape juice and wine. Subtle changes can be observed in the EEM of white wines, where different concentration of sulfur oxide were added to the grape mass of pressing. The resurgence of interest in the Mediterranean diet and its associated health benefits have directed focus on the role that extra virgin olive oil plays. This increasing awareness is leading to increasing product demand, but also opportunities to compromise the quality. Actually, the olive oil adulteration analysis based on near infrared spectrum. But if you look at the PARAFAC Analysis based on this spectra, you will notice that overlapping, there's no differentiation, and we can conclude that near infra spectroscopy is not the best method. In this example, for quantification of blends of extra virgin olive oil varieties, extra virgin olive oil was blended with extra light olive oil. Chemically, each sample was diluted into 3 method of [indiscernible] before 18 measurements, and 5 repetition measurements were plotted. So as you can see in this 3D PARAFAC Analysis, this method can be clearly distinguished the different extra virgin olive oil plants. The A-TEEM technique can also distinguish between different vintages and lots of the same extra virgin olive oil plant product. In the last part of this webinar, I will show 2 applications, the life science domain. Monitoring culture media conditions is highly important for industries in order to improve cell proliferation for applications such as regenerative medicine and protein synthesis. Media stimulates growth and prolongs the viability of the cells. Carbohydrates in the form of sugars are the major source of energy. Most of the media contain glucose and galactose. However, some contain maltose and fructose. Even sub 2 variations in composition could have a noticeable impact on the growth rate of the cell culture and its yield, thus identifying and analyzing cell culture media is really important. Which parameters need to be controlled? Compositional variations, concentration variation, metal variation or impurities and degradation. Analysis of this complex media is challenging because comprehensive analysis generally requires a combination of chromatographic separations and mass spectrometry. This can make routine analysis expensive and time consuming. In this case, fluorescence can help. In biotechnological processes, the fluorescence measurements are based on fluorescent proteins and biological molecules, which show endogenous fluorescence. Among them, you can recall the aminoasis like tryptophan or tyrosine or we can find a coenzyme like the flavin adenine dinucleotide, FAD. [ Dr. Kitagawa ] and coworkers illustrated the utility of A-TEEM to monitor several important components to provide a meaningful insight on the bioreactor condition. And showing this picture, set proliferation was monitored over 4 days. PARAFAC was then used to resolve the various components pieces in the EEM data. Based on this spectroscopy signature, the components were identified as tyrosine, tryptophan and NADPH and riboflavin. From the 4 components identified, NADPH experienced a steady increase as it is a known biomarker for changes in cell environment conditions. The A-TEEM method not only allows to profile free amino acid in cell culture media, but reduces time and cost in comparison to conventional methods. In this study, PCA chemometric analysis provided easy classification. Similar media types, for example, the [ 3D MEM ] mixtures were clustered together and visibly discernible from other media times with no overlap. This result indicates that we can successfully classify different media types using the combination of A-TEEM spectroscopy and chemometrics. Storage conditions have a major impact on degradant formation like the graduation was found to catalyze tryptophan degradation, which in turn reduced cell viability and growth. Several groups have reported developing screening method to detect various degradants, which are often bioproducts of tryptophan, tyrosine and riboflavin. Here, we show the PCA score changes at ambient temperature versus the 4 degree storage temperature. The measurement on Excel media were conducted over 5 days with data points obtaining at 0, 2 and 5 days for both sets of samples stored at different temperatures. The cold store samples cluster together regardless of elapsed times, whereas the ambient conditions clearly affected the sample and led to a change in their molecular fingerprint attributes, resulting in a noticeable trend of changes in the PC scores. Insulin is a hormone made by the pancreas that allows our body to use sugar from carbohydrates in the food that we eat for energy or to store glucose for future use. Insulin helps keep our blood sugar level from getting too high or too low. Many form of insulin treat diabetes. They are grouped by how fast they start to work and how long they effect last. In this study, we investigated 3x of insulin. The human insulin, long-acting insulin, so-called insulin glargine; and the fast-acting insulin instrument, so-called insulin aspart. Because commercial insulin formulation are high in concentration, 4-milligram per milliliter, the inner filter effect correction is very important for measuring the EEM fingerprint, as shown here. The difference between some short-acting and long-acting insulin is, in some cases, only 1 to 3 residues in the protein sequence. This difference in sequence, along with controlled PH of storage and delivery is used to either trigger or prevent the formation of insulin dimers and hexamers in the bloodstream. The formation of these aggregates lets the body absorb insulin more slowly, and the absence of aggregates make it absorbs more quickly. So the question is, can proteins with only 1 or 2 different amino acid between sequences be distinguished by intrinsic fluorescence EEM. So as you can see from this 3D control plot, the reply is yes. The next question is can aggregational state be determined by PARAFAC analysis of intrinsic fluorescence EEM? So the first 5 data points in this picture showed the score for a 4 component PARAFAC model has fit to recombinant human insulin solution at PH 4.5, 7.4 and 8.5. Each of these 5 repeats is at temperature 5, 20, 25, 30 and 37 degrees. This calls for insular glargine in the second grouping, and this calls for the insulin aspart and in the third grouping in this core plot. So insulin glargine, which is sequenced to produce aggregates at biological PH and temperature, shows a much larger score for Component 3, which may be attributed to the blue shift spectrum of tyrosine where insulin is in aggregate form. Solution of human insulin and insulin aspart, which are less likely to aggregate under the same conditions, show much lower scores for the same Component 3. It's evident that insulin sequences that vary by only one, 3 amino acids can be differentiated by intrinsic fluorescence A-TEEM. In conclusion, pure reference is recognized as a highly sensitive tool for the characterization of samples. But combined with other measurement techniques, it becomes even more powerful. HORIBA Scientific has developed a multi-model methodology based on fluorescence and absorption measurements to access with more accuracy than a stand-alone perimeter to samples information. Fast, sensitive and accurate, the A-TEEM methods means more possibilities for the scientific community and analytical world. For more information, on the A-TEEM technique and applications, don't hesitate to check our website, ateem.com. Thanks for your attention.

So I hope you enjoy this webinar. Alessia is there to answer your question. And when she is doing that, you can answer the 3 small questions we have put below volume. So Alessia, I leave you.

Okay. So let me see the first question. How can inner filter effect lead to a wavelength shift? So good question. So as I say, at high concentration, reabsorption may occur. So some amount of fluorescence light emerging from the shorter wavelength component is reabsorbed by the longer wavelength component, which in turn emits higher amplitude. So as a consequence, the maximum of the peak may be red shifted.

So next, the question...

Don't be shy. So what does phenolic mean in wine? So this is a very good question. So the phenolic content in wine refer to the phenolic compounds that can be natural, phenol or polyphenols and -- which include the large group of several under chemical compounds that can affect the taste, the color, the mouthfeel of the wine. The compound includes, for example, [indiscernible], flavonoids, etc. This large group of natural phenols can be broadly separated into 2 main categories, the flavonoids and non-flavonoids. The flavonoids include the [indiscernible], which contribute to the color and the mouthfeel of the wine. The non-flavonoids include a [indiscernible], such as phenolic acid or benzoic acid [indiscernible]. So it's really a complex mixtures.

More questions? So the other question that I see here. So we have 1 question. Can you hear, Alessia?

Yes, I can hear. Yes, I can hear.