TW201925777A - Non-invasive testing method for aflatoxin, food inspection server, and food inspection system - Google Patents

Abstract

A non-invasive testing method for aflatoxin is provided. The method tests a food-to-be-tested by an optical detecting device and a cloud server, and includes the following steps: projecting an excitation beam on the food-to-be-tested by the optical detecting device, such that a reaction beam is emitted from the food-to-be-tested; receiving the reaction beam to obtain a spectral signature of the reaction beam, and transmitting the spectral signature to the cloud server by the optical detecting device; and obtaining an analysis result corresponding to the aflatoxin by the cloud server according to the spectral signature of the reaction beam and an aflatoxin analysis model, where the aflatoxin analysis model is established by the cloud server using a plurality of experimental spectral signature and an intelligent algorithm, where each of the experimental spectral signature corresponds to an aflatoxin content. In addition, a food inspection server and a food inspection system are also provided.

Description

Non-invasive safrole toxin test method, food inspection server and food inspection system

The present invention relates to a method for testing toxins, and more particularly to a non-invasive method for testing food toxins, a food inspection server, and a food inspection system.

The use of near-infrared spectroscopy instruments has been a technology of the past. However, in the past, such traditional testing instruments are not only expensive and bulky, but can only be applied to research units or laboratories, and the near-infrared spectroscopy instruments often require professional training in operation and use. Therefore, it is quite inconvenient for the user side.

In recent years, food safety problems have emerged in an endless stream, so many people hope to be able to test food on their own. However, the concentration of harmful substances that may be present in foods is usually extremely low and is difficult to analyze directly from the spectrum of the food itself. In particular, the sugar content in a food product is usually high (for example, in percentage units), so the sugar content of the food can easily be reflected from the spectral signal of the food itself; the pesticide in the food is usually low in concentration (for example, The 10,000 parts per million (ppm), so it is not easy to distinguish the residue of the pesticide from the spectral signal of the food itself; and the very low concentration of the toxin in the food (for example, in parts per billion (ppb)) It may cause harm to the human body. It is almost impossible to see the concentration from the spectral signal of the food itself by comparing the peak value.

Therefore, at present, in the detection of food, most of the samples must be prepared first, that is, the food to be tested is immersed in the solution to dissolve the target component to be detected, and then the solution containing the target component is detected to be filtered out. Unnecessary noise in the spectrum. However, such detection methods are not practical in application. For food producers, foods that have only been soaked in solution will no longer be available for sale; for consumers, the food that has been bought will be no longer edible.

The invention provides a non-invasive safrole toxin test method, a food inspection server and a food inspection system, which can detect toxins with extremely low concentration in food without preparing samples for food, thereby improving the convenience of food inspection.

The non-invasive xanthine toxin test method of the present invention is suitable for inspecting a food to be tested through an optical inspection device and a cloud server. The non-invasive xanthine toxin test method comprises the steps of: an optical inspection device projecting an excitation beam to a food to be tested, so that the food to be tested emits reaction light; and the optical inspection device receives the reaction light to obtain a spectral signal of the reaction light, and transmits The spectral signal to the cloud server; and the cloud server obtains the analysis result corresponding to the xanthine toxin according to the reaction light spectrum signal and the xanthine toxin analysis model, wherein the cloud server system is established by using multiple experimental spectral signals and intelligent algorithms. A xanthine toxin analysis model, wherein the plurality of experimental spectral signals respectively correspond to a xanthine toxin content.

The food inspection server of the present invention comprises a communication module, a storage device and a processor coupled to the communication module and the storage device. The communication module is configured to receive a response light spectrum signal corresponding to the food to be tested. The storage device is configured to record a plurality of inspection items and a plurality of analysis models corresponding to the inspection items. Multiple test items include safrole toxin, and multiple analytical models include the xanthine toxin analysis model. The communication module further receives one of the inspection items as the item to be tested, and the processor is configured to obtain an analysis result corresponding to the item to be tested according to the reaction light spectrum signal and the analysis model corresponding to the item to be tested, wherein the processor system utilizes more The pen experimental spectral signal and the intelligent algorithm establish a xanthine toxin analysis model, wherein the plurality of experimental spectral signals respectively correspond to a xanthine toxin content.

The food inspection system of the present invention includes an optical inspection device and a food inspection server coupled to the optical inspection device. The optical inspection device is configured to project an excitation beam to the food to be tested to emit a reaction light to the food to be tested, and receive the reaction light to obtain a spectral signal of the reaction light. The food inspection server is configured to obtain the spectral signal and the item to be tested, and obtain an analysis result corresponding to the item to be tested according to the spectral signal and the analysis model corresponding to the item to be tested, wherein the item to be tested is more than the food inspection server. One of the inspection items. The plurality of test items include safrole toxin, and the analysis model includes a xanthine toxin analysis model, wherein the food inspection server obtains the xanthine toxin analysis model using a plurality of experimental spectral signals and a smart algorithm.

Based on the above, in the non-invasive safrole toxin test method, the food inspection server, and the food inspection system provided by the embodiments of the present invention, the food inspection server utilizes a smart algorithm to extract a plurality of inspection items through data exploration technology. The plurality of analysis models are further used to analyze the spectral signals obtained by the optical inspection device. In this way, it is possible to detect toxins having extremely low concentrations in the food without preparing the sample for food, thereby improving the convenience of food inspection.

The above described features and advantages of the invention will be apparent from the following description.

DETAILED DESCRIPTION OF THE INVENTION Reference will now be made in detail to the exemplary embodiments embodiments In addition, wherever possible, the same reference numerals in the drawings

1 is a schematic view of a food inspection system in accordance with an embodiment of the present invention. Referring to FIG. 1, the food inspection system 10 includes an optical inspection device 100 and a food inspection server 200. In the present embodiment, the food inspection server 200 is implemented as a cloud server, and the optical inspection device 100 is connected to the food inspection server 200 via a network to transmit and receive data thereto.

2 is a schematic block diagram of an optical inspection apparatus 100 in accordance with an embodiment of the present invention. Referring to FIG. 2, the optical inspection apparatus 100 includes an excitation light source 110, a spectrometer 120, a processor 130, and a communication module 140. In one embodiment, the components of the optical inspection device 100 are, for example, packaged within the same housing to serve as a portable optical inspection device 100 for easy portability.

The excitation light source 110 is configured to provide an excitation light beam to the food to be tested to generate a reaction light for the food to be tested. For example, the excitation light source 110 is, for example, a laser light source, and provides an infrared light beam as an excitation light beam. In one embodiment, where the user desires to test the xanthotoxin content of the transparent packaged peanuts, for example, the excitation beam is emitted from the exterior of the transparent package to the peanuts inside the package to cause the peanuts to produce a reactive light. However, the present invention does not limit the kind of the food to be tested and the specific means for providing the excitation light beam to the food to be tested, and the technical person skilled in the art can apply the excitation light source 110 of the present embodiment according to the needs thereof.

The spectrometer 120 (Spectrometer) receives the reaction light from the food to be tested by using the light sensing element to obtain the spectral signal of the reaction light of the food to be tested. In one embodiment, the spectrometer 120 is, for example, a Raman spectrometer and obtains spectral signals having a wavelength between 1000 nanometers (nm) and 1300 nm, but the invention is not limited thereto.

The processor 130 is, for example, a central processing unit (CPU), or other programmable microprocessor (Microprocessor), a digital signal processor (DSP), a programmable controller, and a special application. Application Specific Integrated Circuits (ASICs) and Programmable Logic Devices (PLDs) are not limited thereto. In one embodiment, the processor 130 is coupled to the spectrometer 120 for receiving the spectral signals obtained by the spectrometer 120 and transmitting the spectral signals to the food inspection server 200 of the cloud through the communication module 140. In another embodiment, the processor 130 can further transmit the user's identity authentication information, inspection items or other materials to the food inspection server 200 through the communication module 140.

The communication module 140 is connected to the network by wire or wirelessly, and is then connected to the food inspection server 200 to transmit and receive data therewith. In one embodiment, the communication module 140 is, for example, co-connected to the Internet with the food inspection server 200. In another embodiment, the food inspection server 200 can be, for example, a Cable Modem Terminal System (CMTS) provided by a cable TV provider, and the communication module 140 is, for example, a digital subscriber loop (xDSL) or a cable television. A cable modem (Cable Modem) for connecting to the CMTS and transmitting and receiving spectral signals with the food inspection server 150 according to the Data-Over-Cable Service Interface Specifications (DOCSIS). .

3 is a schematic block diagram of a food inspection server in accordance with an embodiment of the present invention. Referring to FIG. 3 , the food inspection server 200 includes a communication module 210 , a storage device 220 , and a processor 230 . The processor 230 is coupled to the communication module 210 and the storage device 220 . In one embodiment, the food inspection server 200 provides a plurality of inspection items. After receiving the spectral signals from the optical inspection apparatus 100, the spectral signals are analyzed based on the at least one inspection item and the analysis results are generated.

The communication module 210 is similar to the communication module 140 of the optical inspection device 100 in the embodiment of FIG. 2, and therefore will not be described herein. In one embodiment, the communication module 210 is for wired or wireless connection to the Internet. In another embodiment, the communication module 210 is configured to receive data such as spectral signals from the communication module 140 according to the DOCSIS, and transmit the analysis result and the like to the communication module 140.

The storage device 220 is used for recording data, such as any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory. (flash memory) or the like or a combination of the above elements, the present invention is not limited thereto. In an embodiment, the storage component 220 includes a database for recording a plurality of inspection items provided by the food inspection server 200 and an analysis model corresponding to each inspection item.

The processor 230 is, for example, a central processing unit (CPU), or other programmable microprocessor (Microprocessor), a digital signal processor (DSP), a programmable controller, and a special application. Application Specific Integrated Circuits (ASICs) and Programmable Logic Devices (PLDs) are not limited thereto. In an embodiment, the processor 230 selects one of the inspection items as the item to be tested, and according to the spectral signal received by the communication module 210 and the analysis model corresponding to the item to be tested in the database of the storage device 220. To use this analysis model to obtain the analysis results of the project to be tested.

For example, when receiving the spectral signal, the communication module 210 receives, for example, the item to be tested (for example, xanthotoxin) to be inspected by the spectral signal. Based on this, the processor 230 searches the database of the storage device 220 for the analysis model corresponding to the item to be tested (for example, the saxitoxin analysis model), and uses the analysis model to analyze the received spectral signal to obtain the analysis result. (for example, xanthine toxin content expressed in parts per billion (ppb)).

It is worth mentioning that the plurality of analysis models corresponding to the plurality of test items recorded in the database of the storage device 220 are, for example, the processor 230 pre-smart algorithm (for example, a deep learning algorithm, a neural network algorithm). And one or a combination of machine learning algorithms) is extracted from experimental spectral signals of various known test item concentrations by data mining techniques. Accordingly, the service provided by the food inspection server 200 will be able to inspect an inspection item of extremely low concentration.

Hereinafter, the non-invasive xanthine toxin test method of the embodiment of the present invention will be exemplarily described using astragalus toxin as a test item.

4 is a flow chart showing a non-invasive xanthine toxin test method according to an embodiment of the present invention. Referring to FIG. 1 to FIG. 4 simultaneously, the non-invasive safrole toxin test method of the present embodiment is applied to the food inspection system 10 described in FIGS. 1 to 3, so that the components in FIGS. 1 to 3 will be combined below. The method of this embodiment will be described.

First, the food inspection server 200 will establish a database of analysis models corresponding to the plurality of inspection items provided. In an embodiment, the communication module 210 first receives a plurality of experimental spectral signals corresponding to the respective inspection items (S410), and the processor 230 uses the intelligent algorithm to perform data scanning techniques according to the respective inspection items. A plurality of experimental spectral signals are used to establish a corresponding analysis model (S420). Subsequently, each inspection item and its corresponding analysis model are recorded in a database of the storage device 220.

For example, the food inspection server 200 provides a detection service for the xanthine toxin content, and thus a plurality of test items include safrole toxin. Therefore, the communication module 210 receives, for example, a plurality of (for example, 3,000) experimental spectral signals, and each experimental spectral signal is obtained, for example, by the optical inspection device 100 from the reaction light of the known content of xanthine toxin in the analyte. of. The processor 230 may obtain a xanthine toxin analysis model based on the experimental spectral signals, for example, using one or a combination of a deep learning algorithm, a neural network-like algorithm, and a machine learning algorithm. After obtaining the xanthine toxin analysis model, the processor 230 has the ability to analyze the content of xanthine toxin. Subsequently, the processor 230 records the xanthine toxin and the corresponding xanthine toxin analysis model in a database of the storage device 220. In addition to the toxoid toxin, the analytical model corresponding to other test items can also be obtained in a similar manner using a smart algorithm, so it will not be described here.

After the database is established, the food inspection server 200 can provide food inspection services. In an embodiment, the optical inspection device 100 transmits the excitation light beam to the food to be tested (for example, peanuts) through the excitation light source 110 to emit the reaction light to the food to be tested (S430), and receives the food to be tested through the light sensing element. The emitted reaction light is used to obtain the reaction light spectrum signal by the spectrometer 120, and the reaction light spectrum signal is transmitted to the food inspection server 200 through the communication module 140 (S440).

It is worth mentioning that the peanut to be tested in the embodiment can be, for example, an unopened bagged peanut in a store, and the user can use the portable optical inspection device 100 to obtain a reaction corresponding to the bagged peanut. Optical spectrum signal. Since the food to be tested has not undergone the process of sample preparation, and the content of the xanthine toxin in the peanut is too low, the obtained reaction light spectrum signal cannot be inferred by simply comparing the peak intensity of the optical signal. The content of scorpion toxin. For analysis, the optical inspection apparatus 100 transmits the response light spectrum signals it has acquired to the food inspection server 200 in the cloud.

In one embodiment, in addition to transmitting the spectral signal of the reactive light, the optical inspection device 100 further transmits the inspection item to the food inspection server 200 to inform the food inspection server 200 of the items to be inspected. In an embodiment, the communication module 210 of the food inspection server 200 receives the reactive light spectrum signal and the inspection item (eg, safrole), and the processor 230 uses the received inspection item as the item to be tested (S450). ). Then, the processor 230 obtains the analysis result corresponding to the item to be tested according to the reaction light spectrum signal and the analysis model corresponding to the item to be tested (for example, the xanthine toxin analysis model) (S460). In this embodiment, the results of the analysis will include the amount of xanthine toxin detected in the bagged peanuts.

In an embodiment, the food inspection server 200 will finally transmit the analysis result to the optical inspection device 100 through the communication module 230 (S470).

It is worth mentioning that the food inspection system 10 of the embodiment of the present invention can be used not only for detecting safrole toxin in peanuts. The food inspection server 200 can expand the inspection items that can be inspected by the method described in the above embodiments by a smart algorithm such as machine learning. In this way, the user only needs to carry the portable portable optical inspection device 100, so that the food to be detected (for example, unopened food, etc.) and the item to be inspected can be located at any place (for example, a store, etc.). Conduct a preliminary test and get the results immediately.

In summary, in the non-invasive xanthine toxin testing method, the food testing server, and the food inspection system provided by the embodiments of the present invention, the food inspection server utilizes a smart algorithm to extract multiple data through data exploration techniques. A plurality of analytical models of the test item are tested, and the analytical models are used to analyze the spectral signals obtained by the optical test device. In this way, it is possible to detect toxins having extremely low concentrations in the food without preparing the sample for food, thereby improving the convenience of food inspection.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention, and any one of ordinary skill in the art can make some changes and refinements without departing from the spirit and scope of the present invention. The scope of the invention is defined by the scope of the appended claims.

10‧‧‧Food Inspection System

100‧‧‧Optical inspection device

110‧‧‧Excitation source

120‧‧‧ Spectrometer

130, 230‧‧‧ processor

140, 210‧‧‧ communication module

200‧‧‧Food Inspection Server

220‧‧‧Storage device

S410~S470‧‧‧Steps for non-invasive xanthine toxin test method

1 is a schematic view of a food inspection system in accordance with an embodiment of the present invention. 2 is a schematic block diagram of an optical inspection apparatus according to an embodiment of the present invention. 3 is a schematic block diagram of a food inspection server in accordance with an embodiment of the present invention. 4 is a flow chart showing a non-invasive xanthine toxin test method according to an embodiment of the present invention.

Claims (10)

A non-invasive xanthine toxin test method, which is suitable for inspecting a food to be tested through an optical inspection device and a cloud server, the non-invasive xanthine toxin test method comprising: the optical inspection device projecting an excitation beam to the food to be tested, The food test device emits reaction light; the optical inspection device receives the reaction light to obtain a reaction light spectrum signal, and transmits the reaction light spectrum signal to the cloud server; and the cloud server according to the reaction light spectrum signal and yellow The scorpion toxin analysis model obtains the analysis result corresponding to the saxitoxin, wherein the cloud server establishes the xanthine toxin analysis model by using multiple experimental spectral signals and a smart algorithm, wherein the experimental spectral signals respectively correspond to a yellow The content of scorpion toxin. The non-invasive safrole toxin test method according to claim 1, further comprising: the cloud server receiving the reactive light spectrum signal and an inspection item, wherein the inspection item is the safrole toxin, and the analysis The result includes the xanthine toxin content of the food to be tested. The non-invasive xanthine toxin test method according to claim 1, wherein the intelligent algorithm comprises one or a combination of a deep learning algorithm, a neural network-like algorithm, and a machine learning algorithm. A food inspection server includes: a communication module for receiving a response light spectrum signal corresponding to the food to be tested; and a storage device for recording a plurality of inspection items and a plurality of analysis models corresponding to the inspection items, wherein the The test items include safrole toxin, and the analysis models include a saxitoxin analysis model; and a processor coupled to the communication module and the storage device, wherein the communication module further receives the test items As a project to be tested, and the processor is configured to obtain an analysis result corresponding to the item to be tested according to the reaction light spectrum signal and the analysis model corresponding to the item to be tested, wherein the processor utilizes multiple experimental spectra The signal and the intelligent algorithm establish the yellow toxin analysis model, wherein the experimental spectral signals correspond to a xanthine toxin content, respectively. The food inspection server according to claim 4, wherein the item to be tested is the safrole toxin, and the processor is configured to obtain the yellow corresponding to the yellow light toxin according to the reaction light spectrum signal and the yellow toxin analysis model. The result of the analysis of saxitoxin, wherein the result of the analysis includes the xanthine toxin content of the food to be tested. The food inspection server according to claim 5, wherein the processor corresponds to each of the inspection items, and the processor uses the intelligent algorithm to obtain the analysis model corresponding to the inspection item according to the plurality of experimental spectral signals, wherein The experimental spectral signals correspond to the known levels of the test items, respectively. The food inspection server of claim 4, wherein the intelligent algorithm comprises one or a combination of a deep learning algorithm, a neural network-like algorithm, and a machine learning algorithm. A food inspection system comprising: an optical inspection device for projecting an excitation beam to a food to be tested to cause the food to be tested to emit reaction light, and receiving the reaction light to obtain a reaction light spectrum signal; a food inspection server coupled And the optical inspection device is configured to obtain the reaction light spectrum signal and the item to be tested, and obtain an analysis result corresponding to the item to be tested according to the reaction light spectrum signal and the analysis model corresponding to the item to be tested, wherein the The item to be tested is one of a plurality of inspection items of the food inspection server, wherein the inspection items include safrole toxin, and the analysis models include a xanthine toxin analysis model, wherein the food inspection server utilizes multiple pens The xanthine toxin analysis model was obtained by experimental spectral signal and intelligent algorithm. The food inspection system of claim 8, wherein the item to be tested is the safrole toxin, and the food inspection server obtains the yellow corresponding to the yellow light toxin analysis model according to the reaction light spectrum signal The result of the analysis of saxitoxin, wherein the result of the analysis includes the xanthine toxin content of the food to be tested. The food inspection system of claim 8, wherein the food inspection server uses the intelligent algorithm to obtain the analysis model corresponding to the inspection item according to the plurality of experimental spectral signals, corresponding to each of the inspection items, The experimental spectral signals correspond to known amounts of the test item.