CN110501303A - The machine and method for sensing the device and method of the characteristic of food materials and infusing coffee - Google Patents

Abstract

A kind of device and method and a kind of machine infused coffee and method of the characteristic sensing food materials.The equipment includes: wave source (20), is configured as transmitted wave to the food materials (22), the wave-length coverage of the wave includes the near-infrared band of selected neighbouring visible band；Detector (26) is configured as detecting the intensity of the wave reflected by the food materials；And analysis module (28), it is configured as determining the characteristic according to the intensity of back wave detected.Preferably, food materials include ground coffee, and characteristic include in following at least any one: the color (baking degree corresponding to ground coffee) of ground coffee；And the fineness of grind of ground coffee.

Description

Apparatus and method for sensing properties of ingredients and machine and method for brewing coffee

technical field

The present disclosure relates to food processing and in particular to coffee brewing.

Background technique

In current consumer grade food processing machines, the protocols for processing food have limited flexibility. Taking a home/office coffee brewer as an example, for a selected flavor, such as espresso or cappuccino, there is always a pre-stored routine for brewing that selected flavor. Such a routine may take into account some user input, such as the volume of coffee or the strength of the coffee.

SUMMARY OF THE INVENTION

In the field of coffee brewing, coffee beans are roasted before grinding and brewing. The degree of roasting can also be marked as light, medium light, medium, medium dark, dark or very dark depending on the color of the roasted beans or ground meal as perceived by the human eye. Darker roasts are generally stronger because they have less fiber content and a sweeter flavor. Lighter roasts have more complex and therefore more intense flavors from aromatic oils and acids that would otherwise be destroyed by longer roast times. Different suppliers can roast coffee beans to different degrees of roasting. Even after grinding, the coffee grounds still reflect how roasted the beans are and give consumers a different coffee taste.

In another example, the fineness of the ground coffee also affects the taste. Generally, finer ground coffee powders contribute to a tasty espresso, however if it is too fine, the resulting coffee brew may be bitter. On the other hand, if the ground coffee is too coarse, the brew may be too light and lack the full-bodied and rich espresso flavor. Again, the fineness of ground coffee varies. Different suppliers may grind the beans to different fines. If a user grinds coffee beans by using a home grinder, the grinder will be worn out after a long period of use, thus resulting in an unstable fineness of the ground coffee powder. Therefore, the fineness of the ground coffee used by the consumer is not always the same and affects the final coffee taste.

Based on the above facts, it can be seen that brewing routines are designed according to coffee grounds having specific characteristics, eg a certain roast level and a certain grind fineness specific to the manufacturer. These specific characteristics may very likely not be satisfied in everyday use. Therefore, a brewing routine when used in the home/office will likely give the coffee a taste that is different or deviate from the desired. Similar problems exist for other application scenarios such as soy milk making, soy making.

In an industrial setting, measuring the degree of roast is carried out by detecting the color of the coffee grounds via NIR spectroscopy, which utilizes a spectrometer system for detection. The wavelength range of NIR is usually 1000nm-2400nm. A light source and sensor suitable for NIR1000nm-2400nm are required. For example, an InGaAs array capable of detecting NIR waves needs to be employed, and the InGaAs array is expensive and complicated to maintain. Therefore, NIR spectrometer systems are difficult to integrate into home coffee machines for sensing the degree of roasting.

Therefore, it would be advantageous to achieve a low-cost solution capable of sensing properties of food ingredients, such as coffee grounds. It would also be advantageous to have an adaptable solution for brewing coffee in the daily use of various coffee grounds.

To better address one or more of these issues, the basic idea of embodiments of the present invention is to use waves in the visible band and the near-infrared band adjacent to the visible band to detect properties of food ingredients. And another basic idea of embodiments of the present invention is to control the grinding/brewing of coffee by taking into account the properties of the coffee grounds.

In a first aspect of the present invention, an apparatus for sensing a property of a food product is proposed, comprising: a wave source configured to emit waves to the food product, the wavelength range of the waves comprising a selected adjacent visible band the near-infrared band; a detector configured to detect the intensity of the wave reflected by the food; and an analysis module configured to determine the characteristic from the detected intensity of the reflected wave.

In this first aspect, the near infrared band adjacent to the visible band is used to detect properties of the food product. Consumer grade sources and detectors are sufficient for this band, so this aspect is low cost and suitable for home/office use. According to experiments, the detection in this band can meet the accuracy requirements in the household appliance level.

In a preferred embodiment, the near infrared band includes a wavelength range of 780 nm to 1000 nm.

In this embodiment, the near-infrared band is further specified. Devices capable of emitting and detecting in this band are less expensive relative to those in the 1000nm-2400nm band, so this embodiment may enable a low cost solution suitable for home/office use.

In a preferred embodiment, said wavelength range of said waves further comprises at least part of the visible band.

In this embodiment, the information contained in the visible band is also detected to determine the properties of the ingredient. Accuracy was further improved.

In a preferred embodiment, the visible band includes a wavelength range of 500 nm to 780 nm.

In this embodiment, the visible band is further specified. According to experiments, the detection in these strips can meet the accuracy requirements in the level of household instruments.

In a preferred embodiment, the detector comprises a silicon based sensor.

In this embodiment, a silicon based detector such as a CMOS sensor (which is inexpensive and widely used in digital cameras and mobile phones) can be used to detect the reflected waves. In this embodiment, the cost is low. Additionally, this embodiment also enables possible integration of the device into a portable device with a camera.

In a preferred embodiment, the apparatus further comprises: a filter configured to filter the reflected waves and obtain a subset of waves in predetermined subbands within the wavelength range; the detector configured to detect the An intensity subset of the intensity of each subband wave in the wave subset; and the analysis module is configured to determine the characteristic from the detected intensity subset.

In this embodiment, only the waves in the predetermined sub-band, rather than the full band, are detected and processed to determine the properties of the ingredient. First, this solution can reduce system complexity, since the components for detecting subband waves are less complex in structure and processing these subband signals is faster than processing full-band signals. Second, experiments have demonstrated that detection in predetermined sub-bands also ensures that the accuracy of the sensing is at an acceptable level for household appliances.

In a preferred embodiment, the apparatus further comprises a memory for storing a model for calculating the properties of the food product from the intensity subset; and the analysis module is further configured to input the detected intensity subset into the model and obtain an output by the model characteristics of food.

In this example, the properties of the food material and the intensities of the sub-band waves are obtained from prior experiments by, for example, the manufacturer. Previous experiments used food materials with various reference properties, and intensity subsets of the intensities of their corresponding sub-band waves were used to build a model or so-called classifier. During daily use, the actual detected intensities of the sub-band waves are entered into the model, and the model will calculate the properties of the detected ingredients. The sensing accuracy is high and sufficient for home use.

In a preferred embodiment, the filter comprises any one of: a filter wheel having a plurality of filter lenses, the lenses corresponding to each of the subbands, the wheel being configured to be rotated to obtain each subband wave; and a grating configured to separate each subband wave from the reflected wave.

This example presents two implementations of the filter. The filter wheel solution can be used with time division detection, where the filter wheel is rotated to output one subband wave at a time. And grating solutions can be used with spatial division detection, where multiple sensors are deployed in different locations, each of them for receiving one sub-band wave refracted by the grating.

In a preferred embodiment, the ingredient includes coffee grounds, and the characteristics include at least any of the following: the degree of roasting of the coffee grounds; and the grind fineness of the coffee grounds.

This embodiment presents the application of the invention in coffee brewing and is particularly advantageous for home/office use of coffee machines.

In a second aspect of the present invention, a machine for brewing coffee is proposed, comprising: a first device configured to determine parameters for brewing coffee according to the roasting degree of the coffee powder; and/or a second device , which is configured to determine parameters for brewing coffee according to the fineness of the ground coffee.

In this aspect, the coffee machine may further determine the brewing parameters according to the characteristics of the coffee grounds, such as the degree of roast (also reflected as color) and the fineness of the grind, thus providing an adjustable coffee brewing solution.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

Description of drawings

Features, aspects and advantages of the present invention will become apparent from reading the following description of non-limiting embodiments with the aid of the accompanying drawings.

FIG. 1 shows a schematic structure of a device according to an embodiment of the present invention;

Fig. 2 shows the cut-off wavelength of the high-pass filter of the embodiment of the present invention;

Figure 3 shows a flow chart of brewing coffee according to another embodiment of the present invention;

Figure 4 shows a schematic structure of a coffee machine with a device according to an embodiment of the invention;

Figure 5 illustrates the accuracy of sensing the fineness of the grind according to an embodiment of the present invention.

Detailed ways

As schematically shown in Figure 1, according to a first aspect of the present invention, there is provided an apparatus for sensing properties of a food product. The apparatus comprises: a wave source 20 configured to emit waves to the food product, the wavelength range of the waves including a selected near-infrared band adjacent to the visible band; a detector 26 configured to detect reflections from the food product and an analysis module 28 configured to determine the characteristic from the detected intensity of the reflected wave.

Although there are slight differences in exact wavelength values, the visible band is generally considered to be from about 340 nm to about 780 nm. In this case, the near-infrared band includes a wavelength range of 780 nm to 1000 nm. An advantage of this embodiment compared to current NIR sensing solutions with wavelength ranges above 1000nm is that the device can use low cost detectors suitable for the wavelength range from 780nm to 1000nm, rather than using detectors for wavelengths above 1000nm. Expensive detector for NIR.

In a preferred embodiment, the wavelength range of the waves also includes at least a portion of the visible band. And more specifically, the portion of the visible band includes the wavelength range of 500 nm to 780 nm. Compared to current NIR sensing solutions that collect waves with wavelengths from 1000 nm to 2400 nm, this embodiment has the following advantages: 1. VIS (visible band) light sources are low cost, such as common incandescent or fluorescent lamps; and 2. Make an industrial use of roast information embedded in the color (visible band) of ground coffee, whereas current NIR sensing does not collect waves in the visible band. In a more complex embodiment, the wavelength range of the waves also includes the full range of the visible band, ie from about 340 nm to about 780 nm.

In a preferred embodiment, since detection is in the visible band and selected near-infrared bands adjacent to the visible band, a silicon-based sensor (eg, a CMOS sensor) is sufficient for detecting the intensity of waves in these bands. Silicon-based sensors are lower cost than common NIR sensors such as InGaAs arrays suitable for wavelengths of 1000-2400 nm.

In practice, the above embodiments may be embedded in various implementations. In one embodiment, the device is a stand-alone device. In another embodiment, the device may be integrated into a mobile phone, where the wave source 20 may be the phone's flash, the detector 26 may be the phone's camera, and the analysis module 28 is through dedicated circuitry in the phone or through processing by the phone A software module implementation executed by the server. Both types can be portable. In yet another embodiment, the device may be installed into a food processor, such as a coffee machine. The device senses a characteristic of the food item, and determines a recipe for food processing through the sensed characteristic of the food item. In this case, the device also has a sample window 22 made of transparent glass or plastic, which allows the transmitted and/or reflected waves to pass through. This embodiment will be explained in more detail in the following sections.

The above sections describe the basic components of the device, while the principles of sensing characteristics will be described below. Ground coffee is used as an example to illustrate the principle, but it should be noted that other ingredients such as soybeans are also suitable.

It is known that coffee beans with different degrees of roasting have different powder colors. The color of the coffee powder is related to the degree of roasting of the coffee beans. Color can be represented by the intensity of waves having multiple wavelengths in the visible range reflected by the coffee grounds. Similarly, differences in the degree of baking also result in differences in intensity at NIR wavelengths. It should be noted that the term "color" covers the intensity of waves in both the visible band and the selected near-infrared band adjacent to the visible band.

The spectra in the visible band and the NIR band adjacent to the visible band, ie the intensities of the waves at these wavelengths, can be used to build models or so-called classifiers. The spectra for each of the various roasting degrees were obtained experimentally by the manufacturer. In order to have consistency between experiments and daily use, the experiments should preferably be under the same environmental conditions as the daily use, eg at the same light levels. The spectra and their corresponding bake levels were used to build the model. Models are stored in the device's memory. And the analysis module determines a characteristic, such as the roast degree of the sample coffee grounds, by using the measured spectrum as an input to the model and obtaining the output roast degree of the model. The detailed method of building a model can be viewed as a training process. Those skilled in the art can adapt common training procedures in the art to suit the specific purpose of an application. This disclosure will not give unnecessary details. It should be noted that sensors can be used to sense multiple characteristics. For example, sensors can be used to sense the degree of roasting or the fineness of the grind. In this case, the model will be different for each characteristic. Thus, there may be multiple models stored in memory.

Preferably, instead of analyzing the entire band of the visible band (VIS) and the near-infrared band (NIR) adjacent to the VIS, only predetermined sub-bands are detected and analyzed in order to save the cost of the detector and reduce the processing burden of the analysis module. To this end, in a preferred embodiment, as shown in Figure 1, the device further comprises a filter 24, which is configured to filter the reflected waves and obtain a subset of the waves in predetermined subbands. The predetermined sub-band is in the wavelength range of 500 nm-1000 nm. The number and range of these subbands can be flexibly adjusted according to the accuracy requirements. The higher the accuracy requirement, the more subbands are required to distinguish colors. In an example, wavelength ranges in which the spectra of different colors are significantly different from each other may have more subbands therein, while wavelength ranges in which the spectra of different colors are nearly identical may have fewer or no subbands therein for detection. In a preferred embodiment, the subbands are 500-550 nm, 550-600 nm, 600-650 nm, 750-800 nm, 850-900 nm. It is to be understood that these subbands are merely examples and that other numbers and locations of subbands are suitable and thus are within the scope of the present invention. These subbands can be continuous or discrete.

There are many embodiments of filters. In one embodiment, filter 24 is a filter wheel having a plurality of filter lenses. Each lens corresponds to one of the subbands. The wheel is configured to be rotated to provide each subband wave in a time-division manner. In one case, the lens is a bandpass filter with a passband of that subband, such as a piece of tinted glass. For example, to obtain a subband of 500-550 nm, a bandpass filter with a passband of 500-550 nm can be used. In another case, the lens is a high-pass or band-pass filter, with the pass-band difference being the same as the desired sub-band. Their transmission curves can be shown by way of example in Figure 2, and the difference in passband between every two adjacent filters is the above subband. The analysis module will obtain the intensity of subband A by subtracting the intensity of the wave passed by the higher subband wave from the intensity of the wave passed by the lower subband filter. For example, to obtain a sub-band of 500-550nm, a subtraction can be done between the outputs of two high-pass filters (500nm-(1000nm) and 550nm-(1000nm) respectively).

In another embodiment, the filter includes a grating configured to spatially separate each subband wave from the reflected wave. The detector may comprise a plurality of CMOS sensors, each arranged at a position to receive a separate sub-band.

In the above embodiment, the color of the coffee grounds (corresponding to the roasting degree of the coffee beans) is sensed. In another embodiment, the grind fineness of the coffee grounds is sensed. However, the subbands used for fineness detection may differ from those used for color (toasting) detection. Those skilled in the art will envision how to implement the device for sensing the fineness of the grind based on the above teachings regarding color (degree of bake).

After clarifying the first aspect of sensing the color (roast degree)/grind fineness of the coffee grounds, the following section will clarify the second aspect of optimizing coffee brewing based on the coffee grounds color (roast degree)/grinding fineness .

During the coffee brewing process, water penetrates the pores of the coffee particles and displaces the air between the particles. Soluble elements (from coffee, pre-existing or obtained from hydrolysis) dissolve into the water in the pores of the coffee granules. These elements diffuse through the water in the small pores to the spherical surface of the particle. There is mass transfer by convection from the surface to most of the water surrounding the particle. When making espresso, hot pressurized and vaporized water is forced through the ground coffee. Brewing parameters such as water temperature, brewing time, pressure affect the taste of coffee. Experiments have also shown that the degree of roasting/grinding of the coffee powder affects the taste of the coffee.

Embodiments of the present invention include the step of determining brewing parameters based on the color/grindness of the coffee grounds in order to provide a desired taste. The flow of coffee brewing according to this embodiment is shown in FIG. 3 .

First, coffee beans 30 are roasted in step 31 , and roasted coffee beans are ground in step 32 . Obtain ground coffee 33. Afterwards, the wave intensity 34 is obtained in step 35 . In step 35, the color and/or grind fineness of the coffee grounds is sensed, and the brewing parameters 36 of the brewing process 37 are adjusted. After brewing, coffee 38 is made.

At step 35, depending on the color (roast degree) of the coffee grounds, brewing parameters, such as water temperature, pressure, and brewing time, can be adjusted to extract the flavor of the coffee. The relevant parameters can also be determined by the obtained grinding fineness. For example, if the grind is too fine, the brew time will be set short, or/and the temperature will be set low, or/and the pressure will be set low. On the other hand, if the grind is too coarse, the brewing process can also be adjusted accordingly.

For this purpose, the machine for brewing coffee according to the invention comprises: a first device configured to determine parameters for brewing coffee according to the color of the coffee grounds; and/or a second device configured to The fineness of the grind determines the parameters for brewing coffee.

The coffee machine may obtain the color and/or grind fineness of the coffee grounds via user input. Alternatively, the coffee machine can also automatically detect these characteristics. One solution is ultrasonic testing; and another solution is to integrate a device according to the above first aspect of the invention. This disclosure will clarify this integration.

Figure 4 schematically shows the structure of the coffee machine. The machine comprises a coffee bean holder 1, a device 2 for sensing the fineness/color (roast degree) of coffee grounds according to an embodiment of the invention, a grinder unit 3, a coffee grounds release chute 4, a filter unit 5 and controller 6. The controller 6 comprises the above first device and a second device for controlling brewing parameters.

The device 2 senses the fineness and/or color of the coffee grounds and sends the information to the controller 6 . The controller 6 is an MCU or the like. It processes this information and determines parameters in the brewing process based on the fineness and/or color of the coffee grounds. Parameters may be determined using other information, such as user input information. The coffee machine may have a storage device, such as a ROM or flash memory, to store a mapping table stating the correlation between different fineness levels and/or colors of coffee grounds and their corresponding brewing parameters. The controller 6 may search the stored table and select the brewing parameter corresponding to the detected fineness and/or color. It should be noted that the brewing parameters are not limited to water temperature, brewing time and pressure. All conditions in the brewing process that can be adjusted to contribute to the taste of the coffee fall within the range of brewing parameters.

For coffee machines without a grinder, the device 2 can be installed in the coffee grounds holder. For espresso machines comprising a grinder, the device 2 can also be mounted beside the passage of the coffee grounds between the grinder and the brewing unit, but the mounting position is not limited to the mentioned ones. For example, in one embodiment, as illustrated in FIG. 4 , the apparatus 2 may be mounted on the wall of the grinder, the ground coffee release chute 4 or the filter unit 5 .

Additionally, for coffee machines with a grinder, the quality of the grinder can also be indicated by the fineness of the coffee grounds ground by the grinder. The controller 6 analyzes the sensed fineness of the coffee grounds, and when the fineness is outside a predetermined range, will send a warning message to the user requesting them to check or replace the grinder components, such as the blades.

The above section illustrates the apparatus and method of the present invention for sensing properties of coffee grounds. The following sections will present experimental results obtained from proof-of-principle experiments to illustrate the feasibility and accuracy of embodiments of the present invention.

Degree of roasting (color)

In a proof-of-principle experiment, coffee roasted at three levels was tested. Coffee beans from 8 origins are roasted using a Probat Emmerich roaster and removed at the beginning of the first crack, at the beginning of the second crack, and 1-2 minutes after the second crack (which are considered to be the test samples at light, medium and dark bake levels, respectively). The roasted beans were then ground at each fineness of grind by means of an independent grinder (Rocky DOSER). The grounds were scanned at the test VIS-NIR optical table. The intensity of the reflected wave is analyzed, based on which the color (degree of baking) is classified by a classification algorithm.

In this experiment, a simplified optical sensing configuration was applied, using six high-pass filters with cutoffs at 500 nm, 550 nm, 600 nm, 750 nm, 850 nm and 950 nm (Figure 2).

The classification performance is listed in Table 1.

It can be seen that all three bake levels are detected with >85% accuracy, with 'shallow' at >99%. This accuracy meets the requirements for home/office use.

fineness

Buy eight types of green Arabica beans from different sources including Peru, Colombia, El Salvador, Guatemala, Kenya, Tanzania Kilimanjaro, India, Tanzania. Select green beans to discard bad beans.

Coffee beans were roasted in the laboratory using a navigation roaster (Probat, Emmerich am Rhein, Germany) under constant power input. Each coffee is roasted to three levels (light, medium and dark). The baking process is as follows:

Roast about 50g of green coffee beans each time. Preheat the roaster to 185 degrees before pouring the green beans into the drum. The temperature of the drum decreases as the beans are added. The first cracking of coffee beans usually occurs about 7 minutes into the roast (at about 175 degrees). Samples taken at this stage are considered light bakes. Roasting continued until the onset of the second crack, which occurred at approximately 9 min and 185 degrees, resulting in a medium roast coffee. When the second burst is over, the heat is turned off and the beans are roasted for another minute. Samples taken at this final stage are considered deep roasts. All roasted coffee beans are cooled and stored in an airtight container with desiccant. The temperature of the roaster needs to drop below 150 degrees before the next bake.

To obtain a representative set of coffee samples, all sets of roasting conditions were each applied three times, resulting in a total of 72 coffee samples (8 sources × 3 roast degrees × 3 copies of the roasting process).

The roasted coffee beans were ground to three levels (fine, medium and coarse) using a home coffee grinder (Rocky DOSER, Rancilio, Italy) with fineness adjusters set at 0, 20 and 50 respectively. A total of 216 ground coffee samples (72 x 3 particle sizes) were obtained using three levels of grind fineness. All roasted coffee beans are ground and labeled and stored in airtight containers with desiccant.

The sample was placed in a 10mm x 10mm glass cuvette and scanned in a laboratory sensing setup using 6 bandpass optical filters with a wavelength range between 500nm and 1050nm. Three copies were taken for each individual sample at different locations in the test tube.

Data were collected in a data acquisition unit and processed in Matlab, where the grinding fineness was classified by a polynomial logistic regression algorithm. The results are shown in FIG. 5 . A fineness of 50 is fine, 52 is medium, and 54 is coarse. For each fineness, the first bar represents the sensitivity, the second bar represents the PPV, and the third bar represents the accuracy of the classification of coffee grind fineness. It can be seen that the average accuracy exceeds 88%.

Modifications to the disclosed embodiments can be understood and effected by those skilled in the art from a study of the specification, drawings, and appended claims. For example, in the above embodiments, the device 2 for sensing the properties of coffee grounds is integrated into the coffee machine. In alternative embodiments, device 2 may be a stand-alone device, or implemented in a mobile phone by multiplexing the mobile phone's flash, camera and processor.

The term "ingredient" covers any material that is to be processed to make something edible or drinkable. Ingredients can be eaten or drunk directly. Alternatively, it cannot be eaten directly but can be processed or brewed to make food or beverages, such as coffee grounds or tea leaves. The word "comprising" does not exclude the presence of elements or steps not listed in a claim or in the specification. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. In the practice of the present invention, several technical features in the claims may be embodied by one component. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.

Claims (15)

1. a kind of machine infused coffee, comprising:

- the first equipment is configured as determining the parameter infused coffee according to the baking degree of ground coffee；And/or

- the second equipment is configured as determining the parameter infused coffee according to the fineness of grind of ground coffee.

2. machine described in claim 1 is configured as obtaining the baking degree and/or grinding of ground coffee by user's input Fineness.

3. machine described in claim 1 is configured as detecting the baking degree and/or fineness of grind of ground coffee automatically.

4. machine as claimed in claim 3, be configured as detecting automatically by ultrasound detection the baking degree of ground coffee and/or Fineness of grind.

5. machine as claimed in claim 3 is integrated with the baking degree and at least one of fineness of grind for sensing ground coffee Equipment (2), the equipment include:

Wave source (20) is configured as transmitted wave to the ground coffee, and the wave-length coverage of the wave includes at least portion of visible band Point and neighbouring visible band seleced near-infrared band,

Detector (26) is configured as detecting the intensity of the wave reflected from the ground coffee, and

Analysis module (28) is configured as determining the baking degree of ground coffee according to the intensity of back wave detected And/or the fineness of grind of ground coffee.

6. machine described in claim 5, wherein the near-infrared band is in the wave-length coverage of 780 nm to 1000 nm.

7. machine described in claim 5, wherein visible band is described at least partially in 500 nm to the wave-length coverage of 780 nm It is interior.

8. machine described in claim 5, wherein the detector (26) includes the sensor based on silicon.

9. machine described in claim 5, further comprises:

Filter (24) is configured as being filtered the wave reflected from the ground coffee and obtains the wave in predetermined sub-band Subset, the predetermined sub-band in the wave-length coverage,

Wherein the detector (26) is configured as detection intensity subset, and the intensity subset includes in the every of marble concentration The intensity of a subband wave, and

Wherein the analysis module (28) is configured as determining the baking degree of ground coffee according to intensity subset detected And/or the fineness of grind of ground coffee.

10. machine described in claim 5, further comprises:

Memory stores described in the baking degree and/or ground coffee for calculating ground coffee according to intensity subset The model of fineness of grind,

Wherein the analysis module (28) be configured to by intensity subset input model detected and obtain by The baking degree of the ground coffee of model output and/or the fineness of grind of ground coffee.

11. machine as claimed in claim 9, wherein the filter (24) includes any of following:

Filter wheel with multiple filter lens, each lens correspond to each subband in each subband, the filtering Device wheel is configured as being rotated to obtain subband wave, and

Grating is configured as separating out each subband wave from the wavelength-division reflected.

12. a kind of method infused coffee, comprising steps of the baking degree and/or fineness of grind of sensing ground coffee, and by Brewing parameter is determined according to baking degree in the first equipment, and/or is determined by the second equipment according to the fineness of grind of ground coffee Brewing parameter, to extract the expectation taste of coffee.

13. method described in claim 12, wherein at least one in the baking degree of ground coffee and the fineness of grind of ground coffee It is a sensed by following steps:

Transmitted wave to the ground coffee, the wave-length coverage of the wave include visible band at least partly and the warp of neighbouring visible band The near-infrared band of selection,

The intensity of the wave reflected by the ground coffee is detected, and

The grinding of the baking degree and/or ground coffee of ground coffee is determined according to the intensity of back wave detected Fineness.

14. method described in claim 13, further comprises step:

The back wave reflected from the ground coffee is filtered, and obtains the marble collection in predetermined sub-band, it is described pre- Stator band in the wave-length coverage,

The detecting step further comprises:

Detection intensity subset, the intensity subset include the intensity for each subband wave concentrated in the marble,

The determining step further comprises:

The baking degree of ground coffee and/or the fineness of grind of ground coffee are determined according to intensity subset detected.

15. method described in claim 13, wherein the determining step further comprises:

The baking degree and/or coffee of ground coffee are determined by the way that the intensity subset of the detection to be matched in correlation The fineness of grind of coffee powder, the correlation are the baking degrees and/or ground coffee that be scheduled and reflecting ground coffee The fineness of grind and its respective strengths subset between relationship.